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no-repeat 6px;background-position:center;margin-top:10px;min-width:55px;display:none;} @media (min-width:768px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .expand_btn{display:none;}} .iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{-webkit-flex:9;-ms-flex:9;flex:9;text-align:left;padding:21px 37px 10px 0px;font-size:16px;font-weight:700;} @media (min-width:768px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{padding:19px 18px 10px 18px;}} @media (min-width:992px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{padding:19px 21px 10px 21px;}} .iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption 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no-repeat 6px;background-position:center;padding:16px 0 16px 0;min-width:62px;max-width:62px;} @media (min-width:768px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{padding:16px 0 17px 0;min-width:52px;max-width:52px;}} @media (min-width:992px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{padding:15px 0 17px 0;min-width:60px;max-width:60px;}} .iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url:hover{-webkit-text-decoration:none;text-decoration:none;} .iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption-text{font-size:14px;padding:0px 18px 17px 22px;} @media (min-width:768px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption-text{padding:0px 15px 15px 18px;}} @media (min-width:992px){.iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption-text{padding:0px 18px 19px 21px;}} .iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption-text figcaption{margin:0;padding:0;} .iASKvs .floats-partial-section .floats-partial-footer .floats-partial-caption-text svg{max-width:100%;} .iASKvs ol{font-size:16px;padding-left:32px;font-family:STIXGeneral-Regular;line-height:24px;margin:15px 0;} .iASKvs ol li{margin:10px 0;padding-left:10px;word-break:normal;} .iASKvs ol li a{word-break:break-all;} .iASKvs ol li .reflinks{display:block;} .iASKvs ol li .reflinks a{color:#4D8A17;-webkit-text-decoration:none;text-decoration:none;cursor:pointer;} .iASKvs ol li .reflinks a:hover{-webkit-text-decoration:underline;text-decoration:underline;} .iASKvs ol li .reflinks .sep{color:#000000;} .iASKvs .table-wrapper{width:auto;overflow-x:auto;} .iASKvs .table-wrapper::-webkit-scrollbar{-webkit-appearance:none;width:5px;height:5px;} .iASKvs .table-wrapper::-webkit-scrollbar-thumb{border-radius:5px;background-color:rgba(0,0,0,.5);-webkit-box-shadow:0 0 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class="articleContent__ArticleContentsSectionsWrapper-sc-2yl9jy-1 jntdRu article_contents__sections_wrapper"> <div class="articleContent__ArticleWrapper-sc-2yl9jy-2 hAzjHP"> <div class="sc-chbbiW mBGSe article_header_class"> <p class="sc-kZmsYB bkiEfx article_type">研究文章|开放获取</p> <div class="sc-RcBXQ ztvYg article_citation"> 体积 2020 <span class="sc-fZwumE cUsFHx">|</span>文章ID. 8829432 <span class="sc-fZwumE cUsFHx">|</span> <a class="sc-htpNat bUhGXt link sc-fQejPQ duuwge" href="https://doi.org/10.1155/2020/8829432" aria-label="Doi-link">https://doi.org/10.1155/2020/8829432</a> </div> <div class="sc-LKuAh lcOiDF article_full_citation"> <div class="sc-hzNEM gsmOCv isHide"> <p>Shuyue Zhou，Xiaobo Li，Yihong Dong，Hao Xu那 “<span class="adjust-article-svg-size">用于多标签分类的解耦和双向重采样方法，具有标签并发的不平衡数据</span>“，<i>科学的规划</一世>那 卷。2020那 文章ID.8829432那 10   页面那 2020． https://doi.org/10.1155/2020/8829432</p> </div> <button class="sc-hSdWYo etauus sc-iBEsjs kPshFw" color="">显示引文</button> </div> <h1 class="sc-iRbamj jZOxOO sc-iSDuPN drIPpp article_title adjust-article-svg-size heading-title">用于多标签分类的解耦和双向重采样方法，具有标签并发的不平衡数据</h1> </div> <div class="sc-jrIrqw eHMFfo article_authors"> <hr class="sc-cEvuZC XkrFe hr__author"> <span class="">水岳周<a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0002-2355-3738" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>那<sup>1</sup></span> <span class=""><b>小波李</b><a class="sc-htpNat bUhGXt link" href="mailto:" aria-label="Mail Option"><span class="sc-htoDjs kNoiZu sc-hjRWVT irNWJI icon-colored _2zt-1j0LmaF8UjTQAM413G icon-themeable" title="" font-size="16"></span></a><a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0001-8343-3124" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>那<sup>2那3.</sup></span> <span class="">依河东那<sup>1</sup> 和</span> <span class="">郝旭<a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0002-6627-4528" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGlkPSJMYXllcl8xIiBkYXRhLW5hbWU9IkxheWVyIDEiIHZpZXdCb3g9IjAgMCAyOTkuNSAyNTkuNSI+PGRlZnM+PHN0eWxlPi5jbHMtMXtmaWxsOiNhNmNlMzk7fS5jbHMtMntmaWxsOiNmZmY7fS5jbHMtM3tmaWxsOm5vbmU7fTwvc3R5bGU+PC9kZWZzPjx0aXRsZT5vcmNoaWQ8L3RpdGxlPjxwYXRoIGNsYXNzPSJjbHMtMSIgZD0iTTI4Ny41LDEyOS41YTEyOCwxMjgsMCwxLDEtMTI4LTEyOEExMjgsMTI4LDAsMCwxLDI4Ny41LDEyOS41WiIvPjxwYXRoIGNsYXNzPSJjbHMtMiIgZD0iTTExNy44LDE4Ny43SDEwMi40VjgwLjZoMTUuNFYxODcuN1oiLz48cGF0aCBjbGFzcz0iY2xzLTIiIGQ9Ik0xNDAuNCw4MC42SDE4MmMzOS42LDAsNTcsMjguMyw1Nyw1My42LDAsMjcuNS0yMS41LDUzLjYtNTYuOCw1My42SDE0MC40Wm0xNS40LDkzLjNoMjQuNWMzNC45LDAsNDIuOS0yNi41LDQyLjktMzkuNywwLTIxLjUtMTMuNy0zOS43LTQzLjctMzkuN0gxNTUuOHY3OS40WiIvPjxwYXRoIGNsYXNzPSJjbHMtMiIgZD0iTTEyMC4yLDU4LjNhMTAuMSwxMC4xLDAsMSwxLTEwLjEtMTAuMUExMC4xNCwxMC4xNCwwLDAsMSwxMjAuMiw1OC4zWiIvPjxyZWN0IGNsYXNzPSJjbHMtMyIgd2lkdGg9IjI5OS41IiBoZWlnaHQ9IjI1OS41Ii8+PC9zdmc+" height="16"></a><sup>3.</sup></span> <div class="sc-fHxwqH dMuGGA isHide"> <p><sup>1</sup><span>宁波大学电气工程与计算机科学学院，宁波315211</span></p> <p><sup>2</sup><span>浙江师范大学数学与计算机科学学院，Jinhua 321004</span></p> <p><sup>3.</sup><span>丽水大学工程学院，丽水323000</span></p> </div> <button class="sc-hSdWYo etauus sc-iQtOjA kfDwZY" color="">显示更多</button> </div> <div class="articleContent__EditorWrapper-sc-2yl9jy-3 evMkvU"> <div class="articleContent__TextWrapper-sc-2yl9jy-8 fYrLtk"> <b>学术编辑：</b>ivángarcía-magariño</div> <div class="articleContent__PublicationTimeLineWrapper-sc-2yl9jy-4 fjviok"> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">已收到</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年3月17日</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">修改后的</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年6月25日</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">公认</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年6月29日</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC isPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">发表</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">01年8月20日</span> </div> </div> </div> <article class="sc-jtRlXQ iASKvs article_body "> <div> <div class="xml-content"> <h4 class="header" id="abstract">抽象的</h4> <p>标签不平衡是多标签数据的特征之一，数据不平衡严重影响分类器的性能。在多标签分类中，重采样方法多用于处理不平衡问题。现有的重采样方法通过欠采样或过采样来平衡数据，导致过拟合和信息丢失。重新取样对少数族裔标签有重大影响。此外，多数标签和少数标签在许多情况下的高并发性也影响了分类性能。在本研究中，我们提出了一种双向重采样的方法来解耦多标签数据集。一方面，通过设置解耦终止条件可以降低标签的并发性，另一方面，通过过采样和欠采样的结合可以减少实例信息的丢失和过拟合。通过测量实例的少数标签，选择对少数标签影响较小的实例进行重新采样。在重采样阶段，为了保持数据的原始分布，限制重采样次数。在7个基准多标号数据集上的实验证明了该算法的有效性，特别是在多数标号和少数标号具有高并发性的数据集上。</p> <span class="end-abs"></span> <h4 id="introduction">1.介绍</h4> <p>随着大数据时代的出现，近年来的数据分类受到了很多关注。不平衡数据通常发生在数据分类领域，包括医疗数据。数据不平衡意味着某些类别的实例远远高于其他类别。通常，与平衡数据集相比，大多数算法在处理不平衡数据时表现不佳。分类器的性能被偏置到多数类，并且在少数群体类上会出现更高的错误率。在实际应用中，我们倾向于更加关注少数阶级的正确分类结果;因此，正确识别少数群体比正确识别多数课程更为重要。例如，在肿瘤分类领域中，非讲患者是多数阶级，而肿瘤患者是少数阶级[<a href="#B1">1</a>]但我们更关注少数肿瘤患者。这些问题也存在于医学成像分类领域，信用卡欺诈的领域存在[<a href="#B2">2</a>]检测和网络入侵识别等。</p> <p>多标签不平衡问题不同于传统的不平衡问题。在多标签不平衡问题中，每个实例都与一组标签相关联，而不是像二元分类法中那样只有一种标签类型。实例数较多的类称为majority类，与majority标号对应;实例数较少的类称为minority类，与minority标号对应[<a href="#B3">3.</a>-<a href="#B5">5.</a>］．例如，在药物靶标的预测中，由于每个药物分子可以对应多个靶标，靶标也可以对应多个药物分子，但有些靶标所包含的实例远远少于其他靶标，这就大大增加了分类的难度。</p> <p>在传统的二值分类中，由于每个样本只对应一个类，所以不需要考虑不同类的影响。但多标签分类面临着新的挑战，在某些情况下既包含多数标签又包含少数标签。这两种标签是高度并发的，这使得正确地对多标签数据进行分类更加困难。多标签数据的不平衡以及多标签和少标签的并发常常并存。当同时处理这两个问题时，往往需要把它们放在一起考虑。</p> <h4 id="related-work">2.相关工作</h4> <p>根据实例标签的数量，将不平衡数据分为单标签的不平衡数据和Multilabel不平衡数据。本节将介绍传统单标签的重采样方法，传统单标签不平衡数据和多标签不平衡数据，并详细描述其优点和缺点。</p> <p>在传统的单标签不平衡数据处理方法中，相关研究可分为三个方面:算法级方法、代价敏感学习方法[<a href="#B6">6.</a>-<a href="#B8">8.</a>和数据级方法。在算法级方法中，对分类算法进行了改进，以适应不平衡数据集。改进算法通常通过移动决策边界来增强少数标签实例的存在性。洪等人[<a href="#B9">9.</a>通过改进内核分类器来优化不平衡数据集的分布。刘等。[<a href="#B10">10</a>]应用加权GINI指数（WGI）选择特征的子集，这有利于准确确定少数阶级。成本敏感的学习通过惩罚错误分类来实现正确分类的目标。数据级方法主要关注重采样方法，包括underAppling [<a href="#B11">11</a>),过采样(<a href="#B12">12</a>和SMOTE方法(合成少数过采样技术)。通过删除数据集中多数类的实例或增加少数类的实例来平衡数据。Galar等[<a href="#B13">13</a>[比较常见的不平衡学习算法，并证明数据预处理与其他分类方法相结合是一种有效的不平衡分类方法。康等。[<a href="#B14">14</a>通过在执行重采样过程之前结合噪声滤波器提出了一种噪声过滤的下采样方案（NUS）。</p> <p>虽然Multilabel的处理也基于数据级方法和算法级方法，但是传统的不平衡数据处理方法并不完全适用于多标签不平衡数据集。在以前的方法中，算法级方法主要专注于调整现有的分类方法，以适应不平衡数据[<a href="#B15">15</a>那<a href="#B16">16</a>］．传统的多书[<a href="#B17">17</a>]分类是将多标签问题转换为两类[<a href="#B18">18</a>那<a href="#B19">19</a>或多重问题[<a href="#B20">20.</a>那<a href="#B21">21</a>]，例如Label Powerset (LP) [<a href="#B22">22</a>]和二进制相关性（BR）[<a href="#B4">4.</a>］．张等人。[<a href="#B23">23</a>]改进了传统的分类算法并提出了Cocoa算法，它将原始MultiLabel数据集转换为一个二进制数据集和每个标签的多个多字符数据集，并将每个数据集重新列到构建不平衡分类器的目的。</p> <p>数据级方法改变实例的分布，以实现数据集的平衡。这些方法主要集中在重采样方面，包括过采样从少数类中生成新的实例和欠采样从多数标签中删除一些实例。在多标签不平衡数据处理方法中，基于数据级的方法值得重视，因为该方法具有以下优点:(1)独立于分类过程，在不干扰分类算法的情况下应用;(2)任务的分离使得不同的算法可以发挥各自的优势。因此，一些研究者对此进行了相关的研究。2015年，Dendamrongvit和Kubat提出了数据级LP-RUS(基于lp的随机欠采样)和LP-ROS(基于lp的随机过采样)[<a href="#B24">24</a>]算法及其改进的算法ML-RU（Multilabel随机缺口）和ML-ROS（Multilabel随机过采样）[<a href="#B25">25</a>］．</p> <p>LP-RUS和LP-ROS方法都通过考虑数据集的标签集来决定如何重新采样。LP-RUS删除出现频率最高的标签集实例，LP-ROS克隆出现频率最低的标签集实例。在重采样过程中，LP-RUS和LP-ROS可能会导致一些标签出现新的不平衡。为了平衡数据集，ML-ROS算法随机复制少数标签相关的实例，增加数据集中少数标签出现的频率，ML-RUS算法随机删除多数标签的实例数量，减少实例集中多数标签出现的频率。</p> <p>ML-ROS和ML-RU重新采样数据集，从而提高了分类性能。但是，存在一些缺点：（1）仅使用超采样或欠采样，因此少数标签信息的冗余和多数标签信息的损失;（2）这些方法破坏了数据集的原始分布，对分类产生不利影响[<a href="#B26">26</a>];（3）他们不能平衡多数标签和少数民族标签的高度同时实例。</p> <p>为了缓解少数标签和多数标签高度同时存在的问题，Charte等[<a href="#B27">27</a>]提出了补救、补救hcr - ros(补救随机过采样杂交)和补救hcr - hus(补救启发式欠采样杂交)。补救算法独立于重采样算法，可与多种重采样算法结合，解耦多数标签和少数标签，降低标签之间的并发程度[<a href="#B28">28</a>］．remedial - hrr - ros将高度并发的标签解耦，然后寻找与少数标签链接的实例，并从它们生成克隆。remedial - hcr - hus对高并发标签解耦，并采用欠采样处理。但这些算法存在以下几个问题:(1)算法并没有从根本上改变原有的缺点，仍可能造成严重的过拟合或信息丢失;(2)算法将数据集分为两部分。即使在解耦过程中解决了高并发问题，但解耦还会继续，分类过程中可能会出现过拟合。</p> <h4 id="our-approach">3.我们的方法</h4> <p>在本节中，我们提出了一种多标签解耦双向重采样算法(ML-DBR)。</p> <h5 id="sec3.1">３.１.相关定义</h5> <p>在研究多标签数据的不平衡问题中，为了测量数据不平衡程度，有两个测量指示指示器可以区分不同标签在多标签不平衡数据中：每标签（IR）的不平衡比和平均不平衡比（平均值）。</p> <p>让Multilabel DataSet<一世>D.</一世> = {(X<sub><i>一世</一世></sub>那<一世>L.</一世><sub><i>一世</一世></sub>）|0≤. <一世>一世</一世> ≤ <一世>N</一世>那<一世>L.</一世><sub><i>一世</一世></sub> ∈ Y}, where X<sub><i>一世</一世></sub>代表<一世>一世</一世>DataSet的实例，<一世>L.</一世><sub><i>一世</一世></sub>是x的标签集<sub><i>一世</一世></sub>，y是数据集的标签集。<span class="equation_break" id="EEq1"><span class="left_break" id="L1"></span><span class="middle_break"><span class="center_break" id="M1"> <svg height="86.6919pt" id="M1-1" version="1.1" viewbox="0 0 32.295 86.6919" width="32.295pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B1-1"> <g transform="matrix(.013,0,0,-0.013,0,25.754)"> <path 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</g> <g transform="matrix(.013,0,0,-0.013,10.96,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span></p> <p>在等式（<a href="#EEq3">3.</a>），<一世>N</一世>是数据集中的实例数，以及等式（<a href="#EEq3">4.</a>），<一世>K.</一世>X的标签数是多少<sub><i>一世</一世></sub>和红外<sub><i>一世</一世></sub>IR是多少<一世>L.</一世><sub><i>一世</一世></sub>．</p> <h5 id="sec3.4">3．4．Min-SCUMBLE</h5> <p>相关研究(<a href="#B27">27</a>]表明少数群体标签实例的重采样对实例中包含的其他少数标签的影响最大。重新采样某个标签时，此标签的重新采样还将其他少数群体标签中包含在实例中，这将干扰其他少数标签的重新采样。基于摇滚度量标准，我们提出了一个Min-Scumble度量标准，在重新采样时，在实例中对少数群体标签进行特别措施：<span class="equation_break" id="EEq5"><span class="left_break" id="L4"></span><span class="middle_break"><span class="center_break" id="M4"> <svg height="44.4934pt" id="M4-1" version="1.1" viewbox="0 0 124.368 44.4934" width="124.368pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B4-1"> <g transform="matrix(.013,0,0,-0.013,0,27.062)"> <use xlink:href="#g190-78"></use> </g> <g transform="matrix(.013,0,0,-0.013,11.49,27.062)"> <path 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412 165 412 225C412 308 347 399 219 399C193 399 146 382 104 367L130 550Z" id="g190-161"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.4,9.25)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="87.5274pt" id="IL4" version="1.1" viewbox="0 0 14.96 87.5274" width="14.96pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 37.83)" xlink:href="#B4-4"></use> </svg></span></span>在哪里<一世>K.</一世>是少数民族标签的数量。</p> <h5 id="sec3.5">3．5．MeanSamples</h5> <p>此外，ML-DBR中使用了视图。手段代表所有标签到达平衡状态的所有标签所需的实例数。通过将具有最高出现频率的标签实例除以平均值来计算它：<span class="equation_break" id="EEq6"><span class="left_break" id="L5"></span><span class="middle_break"><span class="center_break" id="M5"> <svg height="35.1602pt" id="M5-1" version="1.1" viewbox="0 0 74.171 35.1602" width="74.171pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B5-1"> <g transform="matrix(.013,0,0,-0.013,0,25.754)"> <use 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</svg> <svg class="alt_label" height="57.2993pt" id="IL5" version="1.1" viewbox="0 0 15.49 57.2993" width="15.49pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 22.72)" xlink:href="#B5-3"></use> </svg></span></span></p> <h5 id="sec3.6">3.6。提出算法</h5> <p>算法中给出了ML-DBR的伪代码<a href="//www.newsama.com/journals/sp/2020/8829432/alg1/" target="_blank">1</a>．算法分为解耦和重采样两个阶段。在第一阶段，解耦策略对高并发标签进行解耦，防止低并发标签实例的解耦(算法中的步骤4-10)<a href="//www.newsama.com/journals/sp/2020/8829432/alg1/" target="_blank">1</a>）.在第二阶段，将过采样和欠采样组合以选择对少数群体标签对重采样影响的情况（步骤11-24以算法为单位<a href="//www.newsama.com/journals/sp/2020/8829432/alg1/" target="_blank">1</a>）.</p> <div class="floats-partial-section partial-other-wrapper" data-section="alg1"> <div class="floats-partial-content fig-content-alg1"> <table class="algorithm-group"> <tbody> <tr> <td> <table class="algorithm" id="alg1"> <tbody> <tr> <td></td> <td>算法ML-DBR:</T.d> </tr> <tr> <td></td> <td>输入：Multilabel数据集<一世>D.</一世>，重采样率<svg height="8.68572pt" id="M6" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"></path> </g> </svg></td> </tr> <tr> <td></td> <td>输出：预处理数据集<一世>D.</一世></td> </tr> <tr> <td></td> <td>解耦策略</T.d> </tr> <tr> <td></td> <td> (1) Calculate samplesToResampling = <svg height="11.439pt" id="M7" style="vertical-align:-2.15067pt" version="1.1" viewbox="-0.0498162 -9.28833 16.9586 11.439" width="16.9586pt" 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330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path> </g> </svg></td> </tr> <tr> <td></td> <td>(22)随机取m个样本<svg height="9.39034pt" id="M29" style="vertical-align:-3.42943pt" version="1.1" viewbox="-0.0498162 -5.96091 7.65486 9.39034" width="7.65486pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M556 393C556 426 537 448 514 448C496 448 478 435 471 420C468 414 462 401 466 394C473 382 480 368 480 346C480 268 392 143 338 67H336C329 163 319 253 309 330S286 448 254 448C215 448 160 383 127 337L143 311C167 344 200 373 208 373S222 365 229 320C246 214 264 66 268 -20C219 -83 131 -171 17 -239L25 -261L137 -235C248 -110 273 -79 335 8C384 77 481 215 520 287C544 332 556 363 556 393Z"></path> </g> </svg></td> </tr> <tr> <td></td> <td> (23) Let the max Min-SCUMBLEIns sample of m samples as<一世>Z.</一世>，放<svg height="9.39034pt" id="M30" style="vertical-align:-3.42943pt" version="1.1" viewbox="-0.0498162 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242 448 217 448C182 448 138 402 92 341L111 321C149 368 169 378 178 378C188 378 198 363 214 320L254 212C185 117 138 65 107 65C98 65 85 69 82 75C79 82 73 86 65 86C44 86 23 60 23 39C23 7 44 -12 71 -12C119 -12 168 33 265 177L306 61C321 17 347 -12 373 -12C413 -12 465 33 507 96L491 119C459 84 432 60 413 60C395 60 378 92 358 148L321 250C341 279 369 310 389 332C417 363 439 382 456 382C466 382 475 376 481 368C486 361 492 358 496 358C513 358 536 381 536 404Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.173,0)"> <path d="M535 230V280H52V230H535Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,20.709,0)"> <path d="M535 230V280H52V230H535Z"></path> </g> </svg>那</span>Samplestores采样<svg height="7.35473pt" id="M32" style="vertical-align:-0.3499303pt" version="1.1" viewbox="-0.0498162 -7.0048 17.598 7.35473" width="17.598pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,2.179,0)"> <path d="M535 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</svg>然后停止算法</T.d> </tr> <tr> <td></td> <td> (26) return<一世>D.</一世></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">算法1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/alg1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> ML-DBR的伪代码。</div> </div> </div> <p>对于每个标签，计算IR和Chinir以确定标签所属的哪个类别。重新采样率<svg height="8.68572pt" id="M34" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z" id="g113-81"></path> </g> </svg>代表数据集的增加或减少的比例。在ML-ROS和ML-RU中，它导致数据集膨胀或缩小比例<span class="nowrap"> <svg height="8.68572pt" id="M35" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-81"></use> </g> </svg>．</span>在ML-DBR,<svg height="8.68572pt" id="M36" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-81"></use> </g> </svg>不是要增加或减少的数据集的比例，而是计算需要调整的实例数量。接下来，我们介绍了在ML-DBR中使用的策略。</p> <h6 id="sec3.6.1">3.6.1。解耦策略</h6> <p>ml-dbr计算数据集中每个实例的剥离值，设置初始括号（<一世>D.</一世>)的数据集为SCUMBLE(<一世>D.</一世>）<sub>1</sub>，并根据衬垫讨论要求的实例解耦（<一世>D.</一世>）<sub>1</sub>，以减少具有高度并发标签的实例。如果剥离（<一世>一世</一世>）>丑闻（<一世>D.</一世>）<sub>1</sub>，克隆实例<一世>D.</一世><sub><i>一世</一世></sub>作为<span class="nowrap"> <svg height="15.3797pt" id="M37" style="vertical-align:-3.9436pt" version="1.1" viewbox="-0.0498162 -11.4361 13.8905 15.3797" width="13.8905pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-69"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,9.958,-5.741)"> <use xlink:href="#g50-31"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,9.893,3.784)"> <use xlink:href="#g50-106"></use> </g> </svg>那</span><i>L.</一世><sub><i>一世</一世></sub>是标签集<一世>D.</一世><sub><i>一世</一世></sub>那<svg height="15.3797pt" id="M38" style="vertical-align:-3.9436pt" version="1.1" viewbox="-0.0498162 -11.4361 11.4804 15.3797" width="11.4804pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-77"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,7.185,-5.741)"> <use xlink:href="#g50-31"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,8.294,3.784)"> <use xlink:href="#g50-106"></use> </g> </svg>是标签集<span class="nowrap"> <svg height="15.3797pt" id="M39" style="vertical-align:-3.9436pt" version="1.1" viewbox="-0.0498162 -11.4361 13.8905 15.3797" width="13.8905pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-69"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,9.958,-5.741)"> <use xlink:href="#g50-31"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,9.893,3.784)"> <use xlink:href="#g50-106"></use> </g> </svg>那</span> <svg height="15.3797pt" id="M40" style="vertical-align:-3.9436pt" version="1.1" viewbox="-0.0498162 -11.4361 11.4804 15.3797" width="11.4804pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-77"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,7.185,-5.741)"> <use xlink:href="#g50-31"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,8.294,3.784)"> <use xlink:href="#g50-106"></use> </g> </svg> = <一世>L.</一世><sub><i>一世</一世>[IR（<一世>y</一世>)≥MeanIR]</sub>那<一世>L.</一世><sub><i>一世</一世></sub> =<一世>L.</一世><sub><i>一世</一世></sub><sub>[IR（<一世>y</一世>)≤MeanIR]</sub>．然后，当数据集中的每1％的实例进行分离时，缝隙（<一世>D.</一世>)重新计算。当渐淡(<一世>D.</一世>）<sub><i>j</一世>-1</sub>- 丑闻（<一世>D.</一世>）<sub><i>j</一世></sub>≥<一世>T.</一世>，认为数据集的高并发性已经解决，在其中<一世>j</一世>意味着解耦<一世>j</一世>％实例，和<一世>j</一世>−1表示解耦到(<一世>j</一世> − 1) % instances. If SCUMBLE(<一世>D.</一世>）<sub><i>j</一世>-1</sub>−渐淡(<一世>D.</一世>）<sub><i>j</一世></sub> <<一世>T.</一世>，继续解耦剥离>剥落的情况（<一世>D.</一世>）<sub>1</sub>．</p> <p>具有高度并发标签的实例可以通过解耦与少数标签和多数标签分开，并且可以根据步骤4找到高度并发标签。从步骤4到步骤6，该实例将分成两个实例。虽然解耦实例的特征是相同的，但标签集是不同的。</p> <h6 id="sec3.6.2">3.6.2。重新采样策略</h6> <p>首先，这个策略随机选择<一世>m</一世>某个标签的实例<一世>y</一世>．mean sample用于限制样本数量，可以平衡样本之间的分布，在重采样时不超过或低于达到平衡所需的样本数量。接下来，生成一个随机的<一世>X</一世>随机挑选<一世>m</一世>实例<一世>y</一世>．使用Min-SCUMBLEIns度量对随机选择的实例进行重新采样，并比较的Min-SCUMBLE<一世>m</一世>为重新采样的少数标签选择少选择实例的实例。如果<一世>y</一世>属于minBags,<一世>X</一世> = Random (0, MeanSamples − |<一世>y</一世>|)，并使用较低的Min-SCUMBLE克隆实例。如果<一世>y</一世>属于Majbags，<一世>X</一世> = Random (0, |<一世>y</一世>| − MeanSamples), and set the label<一世>y</一世>较低的Min-Scrumble到0的实例。</p> <p>在每次重采样结束时，重新计算MeanIR和IR, MeanSamples只记录初始值，在重采样过程中不重新计算，这样不会对数据集的原始分布造成太大影响。研究发现，当MeanIR≤1.5时，对数据集进行重采样对分类器性能的改善有限[<a href="#B25">25</a>，当MeanIR≤1.5时，ML-DBR停止。</p> <h4 id="results-and-discussion">4.结果与讨论</h4> <h5 id="sec4.1">4.1。评估指标</h5> <p>一般多标签分类器的性能可以以各种方式测量，可以分为多种类型：基于示例的基于标签和基于排名的。为了更好地评估不同方法的性能，我们使用基于标签的评估方法。这种方法可以更好地反映大多数标签和少数民族标签的正确分类。基于标签的评价方法有两种类型：大分子水煮和微量。准确性，宏观 -<一世>F</一世>和微量的<一世>F</一世>被选为评估指标[<a href="#B30">30.</a>]为了获得综合评价。对于标签，TP表示真正的阳性，TN代表否定，FP表示误报，FN表示假否定。</p> <p>无论实例是正面还是负的，准确性是正确预测的实例的数量与预测实例总数的比率。准确性计算如下：<span class="equation_break" id="EEq7"><span class="left_break" id="L41"></span><span class="middle_break"><span class="center_break" id="M41"> <svg height="26.8365pt" id="M41-1" version="1.1" viewbox="0 0 170.724 26.8365" width="170.724pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B41-1"> <g transform="matrix(.013,0,0,-0.013,0,17.261)"> <use xlink:href="#g190-98"></use> </g> <g transform="matrix(.013,0,0,-0.013,5.71,17.261)"> <path d="M390 111C344 68 312 56 269 56C212 56 118 102 118 241C118 346 175 401 241 401C277 401 312 388 342 360C350 352 355 349 361 349C372 349 394 371 394 392C394 403 391 411 378 422C362 436 329 449 288 449H287C250 449 190 432 138 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<p>宏观<一世>F</一世>指所有类别的每个统计指标值的算术平均值。宏观的计算方法<一世>F</一世>为式(<a href="#EEq8">10</a>），方程（<a href="#EEq8">8.</a>）和（<a href="#EEq8">9.</a>）是宏观精度（宏观 -<一世>P.</一世>）和宏观召回（宏观 -<一世>R.</一世>）.在等式中（<a href="#EEq8">8.</a>）和（<a href="#EEq8">9.</a>），<一世>P.</一世>和<一世>R.</一世>代表精度并回忆：<span class="equation_break" id="EEq8"><span class="left_break" id="L42"></span><span class="middle_break"><span class="center_break" id="M42"> <svg height="115.77pt" id="M42-1" version="1.1" viewbox="0 0 97.794 115.77" width="97.794pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B42-1"> <g transform="matrix(.013,0,0,-0.013,0,20.415)"> <use xlink:href="#g190-110"></use> </g> <g transform="matrix(.013,0,0,-0.013,10.61,20.415)"> <use xlink:href="#g190-98"></use> </g> <g transform="matrix(.013,0,0,-0.013,16.31,20.415)"> <use xlink:href="#g190-100"></use> </g> <g transform="matrix(.013,0,0,-0.013,21.81,20.415)"> <use xlink:href="#g190-115"></use> </g> <g 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</svg></span></span>在哪里<一世>N</一世>是实例数量，<一世>F</一世>是输入功能的数量，<一世>K.</一世>是标签的数量，还有<一世>LS.</一世>是不同标签集的数量。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab1"> <div class="floats-partial-content fig-content-tab1"> <table class="table-group" id="tab1"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="8"> <hr></td> </tr> <tr class="thead"> <td class="align_left">数据集</T.d> <td class="align_center">实例</T.d> <td class="align_center">标签</T.d> <td class="align_center">d</T.d> <td class="align_center">卡片</T.d> <td class="align_center">MeanIR</T.d> <td class="align_center">渐淡</T.d> <td class="align_center">TCS.</T.d> </tr> <tr> <td class="thead-hr" colspan="8"> <hr></td> </tr> <tr> <td class="align_left">酵母</T.d> <td class="align_center">2417.</T.d> <td class="align_center">14</T.d> <td class="align_center">0.303</T.d> <td class="align_center">4.24</T.d> <td class="align_center">7.20</T.d> <td class="align_center">0．10</T.d> <td class="align_center">12.56</T.d> </tr> <tr> <td class="align_left">安然</T.d> <td class="align_center">1702</T.d> <td class="align_center">53</T.d> <td class="align_center">0．06</T.d> <td class="align_center">3.38</T.d> <td class="align_center">73.95</T.d> <td class="align_center">0.30</T.d> <td class="align_center">17.50</T.d> </tr> <tr> <td class="align_left">TMC2007.</T.d> <td class="align_center">28596</T.d> <td class="align_center">22</T.d> <td class="align_center">0．10</T.d> <td class="align_center">2.16</T.d> <td class="align_center">15.16</T.d> <td class="align_center">0.18</T.d> <td class="align_center">16.37</T.d> </tr> <tr> <td class="align_left">CAL500.</T.d> <td class="align_center">502</T.d> <td class="align_center">174.</T.d> <td class="align_center">0．15</T.d> <td class="align_center">26.04</T.d> <td class="align_center">20.59</T.d> <td class="align_center">0.34</T.d> <td class="align_center">15.60</T.d> </tr> <tr> <td class="align_left">Corel-16k.</T.d> <td class="align_center">13766</T.d> <td class="align_center">153</T.d> <td class="align_center">０．０２</T.d> <td class="align_center">2.86</T.d> <td class="align_center">34.16</T.d> <td class="align_center">0.27</T.d> <td class="align_center">19.72</T.d> </tr> <tr> <td class="align_left">Corel-5k.</T.d> <td class="align_center">5000</T.d> <td class="align_center">161.</T.d> <td class="align_center">0．01</T.d> <td class="align_center">3.52</T.d> <td class="align_center">189.57</T.d> <td class="align_center">０．３９</T.d> <td class="align_center">20.20</T.d> </tr> <tr> <td class="align_left">MediaMill.</T.d> <td class="align_center">43907</T.d> <td class="align_center">101</T.d> <td class="align_center">0.04</T.d> <td class="align_center">4.38</T.d> <td class="align_center">256.41</T.d> <td class="align_center">0.36</T.d> <td class="align_center">18.19</T.d> </tr> <tr class="table-tr"> <td colspan="8"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/tab1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 七个基准测试数据集的描述。</div> </div> </div> <h5 id="sec4.3">4.3。最佳价值<一世>T.</一世>和<一世>m</一世></h5> <p>的参数<一世>T.</一世>和<一世>m</一世>在我们的方法（算法ML-DBR）直接影响算法的性能，因此探索适当的值也很重要<一世>T.</一世>和<一世>m</一世>．<一世>T.</一世>为解耦的阈值。什么时候<一世>T.</一世>很高，有些情况没有解耦。如果<一世>T.</一世>较低时，当标签并发平衡和<一世>T.</一世>应低于不同数据集的SCUMBLE值。在不同的数据集上，SCUMBLE的最低值是0.1，所以<一世>T.</一世> ≤ 0.1.<一世>m</一世>是在每次重采样期间提取的实例数。什么时候<一世>m</一世>很高，可以增加一些情况的频率。什么时候<一世>m</一世>可以选择对少数标签产生更大影响的情况。此外，<一世>m</一世>需要少于少数民族标签的最小实例数。在ML-DBR中，<一世>T.</一世>设置为0.1,0.01和0.001，<一世>m</一世>设置为3、5和7以进行比较。在传统的多标签分类中，最常见的方法是将多标签转化为二元分类问题，如LP和BR。在本文中，LP, BR, ML-kNN [<a href="#B33">33</a>]进行分类，在BR和LP中使用C4.5作为基础分类器。算法中所有参数均作为默认参数，即重采样率<svg height="8.68572pt" id="M45" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-81"></use> </g> </svg>在实验中设定为0.1，这是ML-ROS的最佳重采样率，并且ML-KNN的邻居数量设定为10.在该实验中使用10倍的交叉验证。<一世>酵母</一世>那<一世>安然</一世>那<一世>TMC-2007.</一世>那<一世>CAL500.</一世>，和<一世>Corel16k.</一世>被选为实验数据集。</p> <p>的实验结果<一世>m</一世>和<一世>T.</一世>值如图所示<a href="//www.newsama.com/journals/sp/2020/8829432/fig1/" target="_blank">1</a>-<a href="//www.newsama.com/journals/sp/2020/8829432/fig5/" target="_blank">5.</a>．什么时候<一世>m</一世>= 3时，它在不同的数据集上比<一世>m</一世> = 5 and<一世>m</一世>= 7。在微量测量中<一世>F</一世>，表现<一世>m</一世>= 3远远超过其他两个值。主要的原因是<一世>m</一世>是5和7，它增加了具有高分剥离的一部分情况的频率，并且过度装备比<一世>m</一世> = 3 in the classification. Therefore,<一世>m</一世> = 3 is an appropriate value in the ML-DBR. It is also found in the experiment that the performance at<一世>T.</一世> = 0.01 is better than 0.001 and 0.1. The reason is that the threshold is lower when<一世>T.</一世> = 0.001, and all instances of SCUMBLEIns > SCUMBLE(<一世>D.</一世>）<sub>1</sub>几乎跟解耦，在数据集平衡之后无法终止解耦;什么时候<一世>T.</一世> = 0.1, the threshold is higher, and the decoupling is terminated when the dataset is not balanced. These figures show that<一世>T.</一世>= 0.01,<一世>m</一世> = 3 obtained the best results for most of the datasets and the combination of ML-DBR and ML-kNN classification algorithms has the best effect, and it is better than LP and BR in different measurements, indicating that ML-kNN is more suitable for ML-DBR.</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig1"> <div class="floats-partial-content fig-content-fig1"> <div class="partial-carousel--single-image"> <img alt="" name="8829432.fig.001" src="https://static-01.hindawi.com/articles/sp/volume-2020/8829432/figures/8829432.fig.001.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/fig1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 结果不同<一世>T.</一世>和<一世>m</一世>酵母数据集中的值。不同的颜色代表不同的评估指标，不同的线型代表不同的算法。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig2"> <div class="floats-partial-content fig-content-fig2"> <div class="partial-carousel--single-image"> <img alt="" name="8829432.fig.002" src="https://static-01.hindawi.com/articles/sp/volume-2020/8829432/figures/8829432.fig.002.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图2</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/fig2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 结果不同<一世>T.</一世>和<一世>m</一世>enron数据集中的值。不同的颜色代表不同的评估指标，不同的线型代表不同的算法。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig3"> <div class="floats-partial-content fig-content-fig3"> <div class="partial-carousel--single-image"> <img alt="" name="8829432.fig.003" src="https://static-01.hindawi.com/articles/sp/volume-2020/8829432/figures/8829432.fig.003.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图3</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/fig3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 结果不同<一世>T.</一世>和<一世>m</一世>tmc-2007数据集中的值。不同的颜色代表不同的评估指标，不同的线型代表不同的算法。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig4"> <div class="floats-partial-content fig-content-fig4"> <div class="partial-carousel--single-image"> <img alt="" name="8829432.fig.004" src="https://static-01.hindawi.com/articles/sp/volume-2020/8829432/figures/8829432.fig.004.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图4</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/fig4/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 结果不同<一世>T.</一世>和<一世>m</一世>CAL-500数据集中的值。不同的颜色代表不同的评估指标，不同的线型代表不同的算法。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig5"> <div class="floats-partial-content fig-content-fig5"> <div class="partial-carousel--single-image"> <img alt="" name="8829432.fig.005" src="https://static-01.hindawi.com/articles/sp/volume-2020/8829432/figures/8829432.fig.005.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图5</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/fig5/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 结果不同<一世>T.</一世>和<一世>m</一世>corel-16k数据集中的值。不同的颜色代表不同的评估指标，不同的线型代表不同的算法。</div> </div> </div> <h5 id="sec4.4">4.4。实验和分析</h5> <p>将本文提出的ML-DBR算法与remedial - hwrr - hus、remedial - hwrr - ros三种算法进行比较[<a href="#B28">28</a>]，以及补救的组合[<a href="#B27">27</a>]和LP-ROS。补救措施-HWR-HUS和RemEdial-HWR-ROS算法在先前的实验中取得了良好的结果，特别是在不平衡数据集中。LP，BR和ML-KNN分类器用于对数据集进行分类，并使用十倍交叉验证。在ml-dbr中，<一世>m</一世>值设置为3，<一世>T.</一世>被设定为0.01。重采样率<svg height="8.68572pt" id="M46" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-81"></use> </g> </svg>所有算法的值为0.1，其他参数均为默认值。对每个数据集进行了10次实验，并对结果进行了平均。</p> <p>表<a href="//www.newsama.com/journals/sp/2020/8829432/tab2/" target="_blank">2</a>-<a href="//www.newsama.com/journals/sp/2020/8829432/tab4/" target="_blank">4.</a>显示以准确性评估的实验结果，宏观<一世>F</一世>，和micro-F。最好的结果用粗体突出显示。如表所示<a href="//www.newsama.com/journals/sp/2020/8829432/tab2/" target="_blank">2</a>与其他算法相比，ML-DBR实现了最佳效果。在表格中<a href="//www.newsama.com/journals/sp/2020/8829432/tab3/" target="_blank">3.</a>和<a href="//www.newsama.com/journals/sp/2020/8829432/tab4/" target="_blank">4.</a>，ML-DBR还具有宏观的最佳性能<一世>F</一世>和微量<一世>F</一世>价值观。ML-DBR的性能远远领先于其他算法<一世>Corel16k.</一世>那<一世>安然</一世>那<一世>Corel-5k.</一世>，和<一世>MediaMill.</一世>数据集表示我们所提出的ML-DBR算法在焊缝和TCS更高时获得最佳结果。此外，ML-DBR还在具有较低缝隙和TCS的数据集上具有某些优点。ML-DBR实现了最佳结果<一世>TMC-2007.</一世>数据集。在这一点<一世>酵母</一世>DataSet与Remedial-Hwr-ROS相比，ML-DBR没有获得一些度量的最佳结果。这发生了因为<一世>酵母</一世>DataSet具有较低的滚动滚动和VILIR值，并且在预处理时两种算法之间没有明显的差异<一世>酵母</一世>数据集。在这一点<一世>卡尔- 500</一世>数据集上，ML-DBR的精度没有明显提高，但宏- dbr的精度没有显著提高<一世>F</一世>和微量<一世>F</一世>结果表明，ML-DBR算法的分类精度优于其他算法，表明该算法提高了少数标签的分类精度<一世>卡尔- 500</一世>数据集。通常，ML-DBR和ML-KNN分级器的组合实现了最佳性能。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab2"> <div class="floats-partial-content fig-content-tab2"> <table class="table-group" id="tab2"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="6"> <hr></td> </tr> <tr class="thead"> <td class="align_left">算法</T.d> <td class="align_center">数据集</T.d> <td class="align_center">LP-ROS.</T.d> <td class="align_center">补救措施-HWR-HUS</T.d> <td class="align_center">remedial-hwr-ros</T.d> <td class="align_center">ML-DBR.</T.d> </tr> <tr> <td class="thead-hr" colspan="6"> <hr></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.4038.</T.d> <td class="align_center">0.4179</T.d> <td class="align_center"><b>0.4344</b></td> <td class="align_center">0.4321</T.d> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.3447</T.d> <td class="align_center">0.3523</T.d> <td class="align_center">0.3605</T.d> <td class="align_center"><b>0.3936.</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.5929</T.d> <td class="align_center">0.6027</T.d> <td class="align_center">0.6038</T.d> <td class="align_center"><b>0.6052</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.2069</T.d> <td class="align_center">0.1851</T.d> <td class="align_center"><b>0.2291</b></td> <td class="align_center">0.2284</T.d> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.0951</T.d> <td class="align_center">0.1018</T.d> <td class="align_center">0.1196.</T.d> <td class="align_center"><b>0.1356</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.0864</T.d> <td class="align_center">0.1011</T.d> <td class="align_center">0.0924</T.d> <td class="align_center"><b>0.1028</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.4039.</T.d> <td class="align_center">0.4326</T.d> <td class="align_center">0.4462</T.d> <td class="align_center"><b>0.4697</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.4027</T.d> <td class="align_center"><b>0.4306</b></td> <td class="align_center">0.4191</T.d> <td class="align_center">0.4286</T.d> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.3134</T.d> <td class="align_center">0.3912</T.d> <td class="align_center">0.3903</T.d> <td class="align_center"><b>0.4291.</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.4862</T.d> <td class="align_center">0.6012</T.d> <td class="align_center">0.6068</T.d> <td class="align_center"><b>0.6127</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.1961.</T.d> <td class="align_center">0.1849</T.d> <td class="align_center"><b>0.2336.</b></td> <td class="align_center">0.2315</T.d> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.0958</T.d> <td class="align_center">0.1067</T.d> <td class="align_center">0.1214</T.d> <td class="align_center"><b>0.1338</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.0871</T.d> <td class="align_center">0.0951</T.d> <td class="align_center">0.0894</T.d> <td class="align_center"><b>0.0972</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.4018</T.d> <td class="align_center">0.4276</T.d> <td class="align_center">0.4250</T.d> <td class="align_center"><b>0.4429</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.4046.</T.d> <td class="align_center">0.4219</T.d> <td class="align_center">0.4367</T.d> <td class="align_center"><b>0.4463</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.3875</T.d> <td class="align_center">0.4286</T.d> <td class="align_center">0.4322</T.d> <td class="align_center"><b>0.4468</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.5328</T.d> <td class="align_center">0.6340</T.d> <td class="align_center">0.6379</T.d> <td class="align_center"><b>0.6479.</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.2494</T.d> <td class="align_center">0.2231.</T.d> <td class="align_center"><b>0.2771</b></td> <td class="align_center">0.2741</T.d> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.1028</T.d> <td class="align_center">0.1242</T.d> <td class="align_center">0.1266</T.d> <td class="align_center"><b>0.1453</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.1020</T.d> <td class="align_center"><b>0.1238</b></td> <td class="align_center">0.1136</T.d> <td class="align_center">0.1221</T.d> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.4457</T.d> <td class="align_center">0.4813</T.d> <td class="align_center">0.4879</T.d> <td class="align_center"><b>0.4908</b></td> </tr> <tr class="table-tr"> <td colspan="6"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表2</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/tab2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 不同重采样算法的精度值。</div> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="tab3"> <div class="floats-partial-content fig-content-tab3"> <table class="table-group" id="tab3"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="6"> <hr></td> </tr> <tr class="thead"> <td class="align_left">算法</T.d> <td class="align_center">数据集</T.d> <td class="align_center">LP-ROS.</T.d> <td class="align_center">补救措施-HWR-HUS</T.d> <td class="align_center">remedial-hwr-ros</T.d> <td class="align_center">ML-DBR.</T.d> </tr> <tr> <td class="thead-hr" colspan="6"> <hr></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.4328</T.d> <td class="align_center">0.4592</T.d> <td class="align_center"><b>0.4629</b></td> <td class="align_center">0.4609</T.d> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.3582</T.d> <td class="align_center">0.3863</T.d> <td class="align_center">0.3927</T.d> <td class="align_center"><b>0.4151.</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.5174</T.d> <td class="align_center">0.6138</T.d> <td class="align_center">0.6199</T.d> <td class="align_center"><b>0.6264</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.3041</T.d> <td class="align_center">0.3028</T.d> <td class="align_center">0.3325.</T.d> <td class="align_center"><b>0.3406.</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.0924</T.d> <td class="align_center">0.1057</T.d> <td class="align_center">0.1129.</T.d> <td class="align_center"><b>0.1377</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.1236.</T.d> <td class="align_center">0.1429.</T.d> <td class="align_center">0.1354</T.d> <td class="align_center"><b>0.1438</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.2248</T.d> <td class="align_center">0.2379</T.d> <td class="align_center">0.2406</T.d> <td class="align_center"><b>0.2428</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.4320.</T.d> <td class="align_center"><b>0.4573</b></td> <td class="align_center">0.4512</T.d> <td class="align_center">0.4556</T.d> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.3602</T.d> <td class="align_center">0.3667</T.d> <td class="align_center">0.4021</T.d> <td class="align_center"><b>0.4134</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.5228</T.d> <td class="align_center">0.5854</T.d> <td class="align_center">0.6039.</T.d> <td class="align_center"><b>0.6194</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.3112</T.d> <td class="align_center">0.2935</T.d> <td class="align_center">0.3209</T.d> <td class="align_center"><b>0.3461</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.0941</T.d> <td class="align_center">0.1124.</T.d> <td class="align_center">0.1136</T.d> <td class="align_center"><b>0.1461.</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.1265</T.d> <td class="align_center"><b>0.1479.</b></td> <td class="align_center">0.1323</T.d> <td class="align_center">0.1468</T.d> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.2392</T.d> <td class="align_center">0.2416</T.d> <td class="align_center">0.2473</T.d> <td class="align_center"><b>0.2605</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.4292</T.d> <td class="align_center">0.4760.</T.d> <td class="align_center"><b>0.5374</b></td> <td class="align_center">0.5360.</T.d> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.4338</T.d> <td class="align_center">0.3851</T.d> <td class="align_center">0.4417.</T.d> <td class="align_center"><b>0.4624</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.4864</T.d> <td class="align_center">0.6133</T.d> <td class="align_center">0.6420.</T.d> <td class="align_center"><b>0.6515</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.3543</T.d> <td class="align_center">0.3691</T.d> <td class="align_center">0.3757</T.d> <td class="align_center"><b>0.4204</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.1245.</T.d> <td class="align_center">0.1263</T.d> <td class="align_center">0.1565</T.d> <td class="align_center"><b>0.1829</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.2012</T.d> <td class="align_center">0.2205</T.d> <td class="align_center">0.2187</T.d> <td class="align_center"><b>0.2274</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.2572</T.d> <td class="align_center">0.2938</T.d> <td class="align_center">0.2856.</T.d> <td class="align_center"><b>0.3067</b></td> </tr> <tr class="table-tr"> <td colspan="6"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表3</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/tab3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 宏观<一世>F</一世>不同重采样算法的值。</div> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="tab4"> <div class="floats-partial-content fig-content-tab4"> <table class="table-group" id="tab4"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="6"> <hr></td> </tr> <tr class="thead"> <td class="align_left">算法</T.d> <td class="align_center">数据集</T.d> <td class="align_center">LP-ROS.</T.d> <td class="align_center">补救措施-HWR-HUS</T.d> <td class="align_center">remedial-hwr-ros</T.d> <td class="align_center">ML-DBR.</T.d> </tr> <tr> <td class="thead-hr" colspan="6"> <hr></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.5737</T.d> <td class="align_center"><b>0.6774</b></td> <td class="align_center">0.6743</T.d> <td class="align_center">0.6763</T.d> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.5581</T.d> <td class="align_center">0.5649</T.d> <td class="align_center">0.6016</T.d> <td class="align_center"><b>0.6238.</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.6125</T.d> <td class="align_center">0.7033</T.d> <td class="align_center">0.7072</T.d> <td class="align_center"><b>0.7103</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.4279</T.d> <td class="align_center">0.4213</T.d> <td class="align_center">0.4316</T.d> <td class="align_center"><b>0.4424</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.2374</T.d> <td class="align_center">0.2867</T.d> <td class="align_center">0.3416</T.d> <td class="align_center"><b>0.3652</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.2229</T.d> <td class="align_center">0.2752</T.d> <td class="align_center">0.2433</T.d> <td class="align_center"><b>0.2809</b></td> </tr> <tr> <td class="align_left">LP.</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.5136</T.d> <td class="align_center">0.5625</T.d> <td class="align_center">0.5593</T.d> <td class="align_center"><b>0.5671</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.5917</T.d> <td class="align_center">0.6667</T.d> <td class="align_center"><b>0.6753</b></td> <td class="align_center">0.6742</T.d> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.5784</T.d> <td class="align_center">0.5939.</T.d> <td class="align_center">0.6016</T.d> <td class="align_center"><b>0.6429</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.6379</T.d> <td class="align_center">0.7221</T.d> <td class="align_center">0.7230</T.d> <td class="align_center"><b>0.7248</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.4213</T.d> <td class="align_center">0.4652</T.d> <td class="align_center">0.4679</T.d> <td class="align_center"><b>0.4956</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.2466</T.d> <td class="align_center">0.3455</T.d> <td class="align_center">0.3492</T.d> <td class="align_center"><b>0.3601.</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.2185</T.d> <td class="align_center">0.2673</T.d> <td class="align_center">0.2357</T.d> <td class="align_center"><b>0.2710</b></td> </tr> <tr> <td class="align_left">布尔</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.5278</T.d> <td class="align_center">0.5562</T.d> <td class="align_center">0.5641</T.d> <td class="align_center"><b>0.5698</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">酵母</T.d> <td class="align_center">0.6939</T.d> <td class="align_center">0.6985</T.d> <td class="align_center">0.7011</T.d> <td class="align_center"><b>0.7213</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">安然</T.d> <td class="align_center">0.6061.</T.d> <td class="align_center">0.5732</T.d> <td class="align_center">0.6108</T.d> <td class="align_center"><b>0.6805</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">TMC-2007.</T.d> <td class="align_center">0.7248</T.d> <td class="align_center">0.7225</T.d> <td class="align_center">0.7354</T.d> <td class="align_center"><b>0.7826</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">卡尔- 500</T.d> <td class="align_center">0.5849</T.d> <td class="align_center">0.5320.</T.d> <td class="align_center">0.6287</T.d> <td class="align_center"><b>0.6735</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">Corel-16k.</T.d> <td class="align_center">0.4266</T.d> <td class="align_center">0.4212</T.d> <td class="align_center">0.4597</T.d> <td class="align_center"><b>0.4675</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">Corel-5k.</T.d> <td class="align_center">0.3901</T.d> <td class="align_center">0.4670</T.d> <td class="align_center">0.4524</T.d> <td class="align_center"><b>0.4709</b></td> </tr> <tr> <td class="align_left">ML-KNN</T.d> <td class="align_center">MediaMill.</T.d> <td class="align_center">0.6817.</T.d> <td class="align_center">0.7724</T.d> <td class="align_center">0.7782</T.d> <td class="align_center"><b>0.7815</b></td> </tr> <tr class="table-tr"> <td colspan="6"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表4</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/tab4/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 微-<一世>F</一世>不同重采样算法的值。</div> </div> </div> <p>桌子<a href="//www.newsama.com/journals/sp/2020/8829432/tab5/" target="_blank">5.</a>在应用ML-DBR之后，显示每个数据集的新滚动和VILIR值。与表格相比，呼啸仪和黄色值减少<a href="//www.newsama.com/journals/sp/2020/8829432/tab1/" target="_blank">1</a>，验证提出的ML-DBR算法采用的去耦策略和重采样策略的有效性。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab5"> <div class="floats-partial-content fig-content-tab5"> <table class="table-group" id="tab5"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr class="thead"> <td class="align_left">数据集</T.d> <td class="align_center">MeanIR</T.d> <td class="align_center">渐淡</T.d> </tr> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr> <td class="align_left">酵母</T.d> <td class="align_center">2.43</T.d> <td class="align_center">0.07</T.d> </tr> <tr> <td class="align_left">安然</T.d> <td class="align_center">48.19.</T.d> <td class="align_center">0．23</T.d> </tr> <tr> <td class="align_left">TMC2007.</T.d> <td class="align_center">12.42</T.d> <td class="align_center">0．14</T.d> </tr> <tr> <td class="align_left">CAL500.</T.d> <td class="align_center">18.19</T.d> <td class="align_center">0．24</T.d> </tr> <tr> <td class="align_left">Corel-16k.</T.d> <td class="align_center">27.32</T.d> <td class="align_center">0.21</T.d> </tr> <tr> <td class="align_left">Corel-5k.</T.d> <td class="align_center">69.75</T.d> <td class="align_center">0.26</T.d> </tr> <tr> <td class="align_left">MediaMill.</T.d> <td class="align_center">80.61</T.d> <td class="align_center">0.26</T.d> </tr> <tr class="table-tr"> <td colspan="3"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表5</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/sp/2020/8829432/tab5/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> ML-DBR应用后的黄色和剥离值。</div> </div> </div> <p>总之，与其他重采样算法相比，我们的实验证明ML-DBR在几个多函数重采样算法中具有最佳性能。它可以有效地平衡数据级别的Multilabel不平衡数据。ML-DBR可以有效地处理具有少数群体标签和多数标签的高并发性的Multilabel不平衡数据，并对提高少数群体标签的分类性能产生重大影响。</p> <h4 id="conclusions">5.结论</h4> <p>多标签数据具有多数标签和少数群体标签的不平衡和高并发性的问题。本文提出了数据级的ML-DBR算法。通过解耦多数标签和少数群体标签的高度并发数据并测量重新采样期间标签的影响，减少了标签的不平衡，并保证了实例的独立性。因此，ML-DBR具有以下优点：（1）解耦策略更有效和合理;（2）它结合了欠采样和过采样过程，这可以减少由过采样引起的少数群体标签信息的冗余和由欠采样引起的多数标签信息，从而使实例分布更加平衡并减少对采样期间对少数群体标签的影响过程;（3）数据集的原始分发状态不会更改过多，并且维护数据集的原始分发。实验表明，ML-DBR可以有效地提高分类器的分类性能。ML-DBR在具有高TCS值，大量标签和具有高泡沫值的标签的高并发性的数据集中实现了出色的结果。如何找到更合适的<一世>m</一世>和<一世>T.</一世>不同数据集的值是我们未来工作的重点。</p> <h4 id="data-availability">数据可用性</h4> <p>用于支持本研究结果的数据集已保存在木兰资料库(<a href="http://mulan.sourceforge.net/datasets-mlc.html" target="_blank">http://mulan.sourceforge.net/datasets-mlc.html</a>）.</p> <h4 id="conflicts-of-interest">利益冲突</h4> <p>提交人声明有关本文的出版物没有利益冲突。</p> <h4 id="acknowledgments">致谢</h4> <p>该研究得到了中国国家自然科学基金的支持，否定。61373057，浙江省自然科学基金会授予否。LY20F020009，丽水市科技规划项目授予拒绝。2019RC05。</p> </div> <div class="xml-content"> <h4 class="references" id="references">参考文献</h4> <ol> <li id="B1">G. 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Zhou&publication_year=2007" target="_blank">谷歌学术</a></span></li> </ol> </div> <div class="xml-content"> <h4 class="copyright" id="copyright">版权</h4> <p>版权所有©2020 Shuyue Zhou等人。这是分布下的开放式访问文章<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">创意公共归因许可证</a>如果正确引用了原始工作，则允许在任何媒体中的不受限制使用，分发和再现。</p> </div> </div> </article> </div> </div> </div> <div class="biblio__Wrapper-sc-1nou1lz-0 eyHfWh journal_article__BiblioIntegratorWidget-sc-12fk6zy-5 dbAANp article_widget biblioWrapper hide"> <hr> <h4 class="biblio__H4-sc-1nou1lz-2 kgAKHb hrTitleRef">更多相关文章</h4> <div id="biblio_widget" class="biblio__BiblioWrapper-sc-1nou1lz-1 ObfEZ"></div> </div> </div> </div> <div class="threeColumn__RightWrapper-sc-1bf4078-2 jzTMLK rightWrapper" id="rightSectionMenu"> <div> <div class="journal_article__ArticleStickyTopToolbar-sc-12fk6zy-0 gbrQxi article_sticky_top_toolbar"> <div class="sc-cBdUnI hRnXob article_top_toolbar"> <a class="sc-htpNat bUhGXt link sc-jtRfpW dIjoCg sc-exkUMo hymroZ 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height="24"><span class="sc-dRFtgE jrsDZP">下载其他格式</span><span class="sc-gkFcWv gWTCXQ">更多的</span></span> <span class="sc-htoDjs bMdQcY sc-gbOuXE jGPvPL icon-colored _1CwL9R82TPl2HxDleeYGDR icon-themeable" title="" font-size="11"></span> </div> <div class="sc-fnwBNb etzuBr"> <a class="sc-htpNat bUhGXt link sc-iNhVCk gWgCNb open_panel" href="//www.newsama.com/downloads/journals/sp/2020/8829432.epub" aria-label="ePub" property="background-color" color="#D8D8D8"> <div class="sc-eAKXzc WHkfb"> ePub</div><img alt="" class="sc-EHOje jOLhQl sc-bfYoXt cGuwDn" title="" role="presentation" 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height="24"></a> <a class="sc-htpNat bUhGXt link sc-iNhVCk gWgCNb open_panel" href="//www.newsama.com/downloads/journals/sp/2020/8829432.xml" aria-label="XML" property="background-color" color="#D8D8D8"> <div class="sc-eAKXzc WHkfb"> XML.</div><img alt="" class="sc-EHOje jOLhQl sc-bfYoXt cGuwDn" title="" role="presentation" 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height="24"><span class="sc-dRFtgE jrsDZP">订单印刷副本</span><span class="sc-gkFcWv gWTCXQ">命令</span></span><span class="sc-htoDjs kNoiZu sc-gbOuXE jGPvPL right__icon icon-colored _32KP5uexH-Bgs7Kj8Gk1Tz icon-themeable" title="" font-size="16"></span></a> <div class="sc-hBbWxd kdRRST"> <div class="sc-dyGzUR cHatnS"> <div> 的观点<span>474.</span> </div> <div> 下载<span>313.</span> </div> </div> <div> <div class="sc-drKuOJ kxdGAZ"> <span class="__dimensions_badge_embed__" data-doi="10.1155/2020/8829432" data-style="small_circle"></span>引用</div> </div> </div> <nav class="sc-bRBYWo fXBKFa sc-hUfwpO jHPgUP" id="toolbar__iconNav"> <h3 class="sc-Rmtcm hNPrIr">分享帖子</h3> <ul class="sc-hzDkRC bnbfTS"> <li class="iconBorder"><a class="sc-htpNat bUhGXt link" href="https://twitter.com/share?url=https://doi.org/10.1155/2020/8829432&via=Hindawi" aria-label="Sharing on Twitter" rel="noopener noreferrer" target="_blank"><img alt="" class="sc-EHOje jOLhQl socialTwitter" title="" role="presentation" 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