An Optimizing Selection in a Family of Attribute Reducts
DENG Da-yong1,2, GE Ya-wen2, HUANG Hou-kuan3
1. Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
2. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Abstract：Attribute reduction is one of important applications in rough set theory.There are more than one attribute reduct in a data set,and heuristic algorithms are always used to find one of them,which is verified with experiments.For many attribute reducts,it is hard for people to distinguish them,and lacks of valid methods of selecting the best one or a better one.Indexes of concept drift and information loss are employed to compare the same type of Pawlak attribute reducts in a knowledge system.The focus of attribute reducts is presented,and its properties are investigated in this paper.Experimental results show that the closest attribute reduct to the focus of attribute reducts is better than other attribute reducts in classification accuracy.Indexes of concept drift detection and information loss can distinguish different attribute reducts,and the focus of attribute reducts can be employed to select the best attribute reduct or a better one.
邓大勇, 葛雅雯, 黄厚宽. 属性约简簇的优化选择[J]. 电子学报, 2019, 47(5): 1111-1120.
DENG Da-yong, GE Ya-wen, HUANG Hou-kuan. An Optimizing Selection in a Family of Attribute Reducts. Acta Electronica Sinica, 2019, 47(5): 1111-1120.
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2019, Vol. 47 
