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  • In Jour Smart Sensing And Intelligent Systems

 

Research paper | 10-April-2013

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 448–465

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