Simultaneous classification and feature selection via LOG SVM and Elastic LOG SVM
作者单位：Department of Automation China University of Petroleum Beijing Beijing Aerospace PetroChemical Technology And Equipment Engineering Company Limited
会议名称：《第36届中国控制会议》
会议届次：36
主办单位：Dalian University of Technology;Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
会议日期：2017年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China under Grant No.21006127
关 键 词：Classification Sparse Feature selection Non-convex LOG penalty LOG SVM Elastic LOG SVM
摘 要：In data mining and machine learning,classifying the class labels and selecting features simultaneously are *** study proposes two new sparse support vector machines(SVMs),namely,LOG SVM and Elastic LOG *** LOG SVM uses the LOG penalty,and the Elastic LOG SVM combines the non-convex LOG penalty and the L norm *** LOG SVM and Elastic LOG SVM can achieve classification and feature selection *** quadratic approximation is used to solve both *** are also conducted to show that the proposed SVMs perform well in the aspects of classification and feature selection.