Multi-objective attribute reduction in three-way decision-theoretic rough set model

作     者：Li, Weiwei Jia, Xiuyi Wang, Lu Zhou, Bing 

作者机构：Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Sam Houston State Univ Dept Comp Sci Huntsville TX 77341 USA 

出 版 物：《INTERNATIONAL JOURNAL OF APPROXIMATE REASONING》 (Int J Approximate Reasoning)

年 卷 期：2019年第105卷

页      面：327-341页

核心收录：

学科分类：07[理学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：National Key R&D Program of China [2018YFB1003902] Natural Science Foundation of Jiangsu Province [BK20170809] National Natural Science Foundation of China [61773208, 71671086] China Postdoctoral Science Foundation [2018M632304] 

主　　题：Attribute reduct Three-way decisions Decision-theoretic rough set model Multi-objective optimization problem Ensemble learning 

摘      要：Attribute reduction plays an important role in rough set theory. Many attribute reduction methods have been proposed based on different definitions of attribute reduct, while an attribute reduct can be regarded as a minimal attribute subset that satisfies specific criteria. Most reducts are defined on the basis of a single criterion, which can only affect one specific characteristic of the data or one preference of users. However, the single criterion-based attribute reduct may not meet the requirement of complex problems. To address this problem, based on three-way decision-theoretic rough set model, this paper defines a multi-objective attribute reduct. Three types of criteria for defining attribute reduct including the positive region, decision cost and mutual information are considered and combined to a multi-objective optimization problem. Based on the proposed multi-objective attribute reduct, we also introduce a multi-objective optimization-based attribute reduction method and an ensemble learning-based attribute reduction method. Experimental results on several datasets show that the proposed attribute reduction methods can obtain a robust and better classification performance. (C) 2018 Elsevier Inc. All rights reserved.