TY -的A2 Ozkan易卜拉欣AU - Das, Himansu AU -奈克,Bighnaraj盟——Behera h . s . PY - 2020 DA - 2020/03/01 TI -减少混合去噪和特征分类模型SP - 4152049六世- 2020 AB -模糊系统的演变表明,影响力和成功在许多通用逼近能力和应用程序。提出了一种用于数据分析的神经-模糊-特征约简(NF-FR)混合模型。提出的NF-FR模型对所有模式使用基于特征的类归属模糊化过程。在模糊化过程中，根据数据集中可用类的数量扩展所有特性。它有助于处理不确定性问题，并帮助基于人工神经网络(ANN-)的模型获得更好的性能。然而，由于模糊化过程中输入特征的扩展，问题的复杂性增加了。这些扩展的特性可能并不总是对模型有显著的贡献。为了克服这一问题，采用特征约简(feature reduction, FR)方法过滤掉不重要的特征，使网络的计算成本更低。这些简化的显著特征被用于基于神经网络的模型分类数据。通过10个基准数据集(包括平衡和不平衡数据集)对模型的有效性进行了测试和验证，验证了所提出的NF-FR模型的性能。 The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information. SN - 1687-7101 UR - https://doi.org/10.1155/2020/4152049 DO - 10.1155/2020/4152049 JF - Advances in Fuzzy Systems PB - Hindawi KW - ER -