Feature subset selection based on mahalanobis distance: a statistical rough set method  

作　　者：孙亮 韩崇昭 

机构地区：[1]School of Electronic and Information Engineering, Xi'an Jiaotong University

出　　处：《Journal of Pharmaceutical Analysis》2008年第1期14-18,共5页药物分析学报（英文版）

基　　金：This work was supported by the National Basic Research Program of China(No.2001CB309403)

摘　　要：In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.

关 键 词：feature subset selection rough set attribute reduction Mahalanobis distance 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]