Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

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• 作者：Fannia Pacheco ; ^a ; ^{fannikaro@gmail.com} ; Mariela Cerrada^b ; ^a ; ^{cerradam@ula.ve} ; René ; -Vinicio Sá ; nchez^a ; ^{rsanchezl@ups.edu.ec} ; Diego Cabrera^a ; ^{dcabrera@ups.edu.ec} ; Chuan Li^c ; ^{chuanli@21cn.com} ; José ; Valente de Oliveira^d ; ^jvo@ualg.pt
• 关键词：Attribute clustering ; Rough set ; Feature selection ; Fault severity classification ; Rotating machinery
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：1 April 2017
• 年：2017
• 卷：71
• 期：Complete
• 页码：69-86
• 全文大小：3896 K
• 卷排序：71

文摘

A novel algorithm is proposed for unsupervised feature selection. The algorithm efficacy is evaluated through the accuracy of several classifiers. Adequate attributes are effectively selected for several case studies. The proposal presents better results than other attribute clustering algorithms. The proposal provides similar results to supervised feature selection approaches.