Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Attribute clustering,Rough set,Feature selection,Fault severity classification,Rotating machinery
论文评审过程：Received 20 June 2016, Revised 18 November 2016, Accepted 19 November 2016, Available online 26 November 2016, Version of Record 28 November 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.11.024
