Information entropy based attribute reduction for incomplete heterogeneous data

Author:

Wang Pei1,Qu Liangdong2,Zhang Qinli3

Affiliation:

1. Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, Guangxi, P.R. China

2. School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China

3. School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, Anhui, P.R. China

Abstract

Attribute reduction in an information system (IS) is an important research topic in rough set theory (RST). This paper investigates attribute reduction for incomplete heterogeneous data based on information entropy. Information entropy in an incomplete IS with heterogeneous data (IISH) is first defined. Then, some derived notions of information entropy, such as joint information entropy, conditional information entropy, mutual information entropy, gain and gain ratio in an incomplete decision IS with heterogeneous data (IDISH), are presented. Next, information entropy is applied to perform attribute reduction in an IDISH. Two attribute reduction algorithms, based on gain and gain ratio, respectively, are proposed. Finally, in order to illustrate the feasibility and efficiency of the proposed algorithms, experimental analysis is carried out and comparisons are done. It is worth mentioning that the incomplete rate is used to deal with incomplete heterogeneous data.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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