Attribute selection for heterogeneous data based on information entropy

Abstract

Attribute selection in an information system is one of the important applications of rough set theory. This paper studies attribute selection for heterogeneous data based on information entropy. We first define information entropy in an information system with heterogeneous data and then put forward the notions of joint information entropy, conditional information entropy and mutual information entropy in a decision information system with heterogeneous data. We apply information entropy to perform attribute selection in a decision information system with heterogeneous data. We propose two attribute selection algorithms based on information entropy. Finally, we make experimental analysis and comparisons to illustrate the feasibility and efficiency of the proposed algorithms.

Keywords:

• Fuzzy relation
• uncertainty
• measure
• entropy
• attribute selection
• heterogeneous data

Acknowledgments

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions, which have helped immensely in improving the quality of the paper. This work is supported by National Natural Science Foundation of China (11971420), Special Scientific Research Project of Young Innovative Talents in Guangxi (2019AC20052), Natural Science Foundation of Guangxi (2019JJA110036, AD19245102, 2018GXNSFDA294003, 2018GXNSFDA294134), Guangxi Science and Technology Program(2017AD23056), Key Laboratory of Software Engineering in Guangxi University for Nationalities(2020-18XJSY-03), Guangxi Higher Education Institutions of China (Document No.[2019] 52), Guangxi Higher Education Reform Project (2020XJJGZD17), Research Project of Institute of Big Data in Yulin (YJKY03), Engineering Project of Undergraduate Teaching Reform of Higher Education in Guangxi (2017JGA179) and Research Project for Young and Middle-aged Teachers in Higher Education Institution of Guangxi (2017KY0175).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [11971420].

Notes on contributors

Zhaowen Li

Zhaowen Li received the M. Sc. degree in Mathematics from Guangxi University, Nanning, China, in 1988 and the Ph.D. degree in Mathematics from Hunan University, Changsha, China, in 2008. He is currently a professor in School of Mathematics and Statistics, Yulin Normal University. His research interests include granular computing, rough set theory, data mining, fuzzy set theory and information systems.

Liangdong Qu

Liangdong Qu received the M. Sc. degree in Mathematics from Guangxi University for Nationalities, Nanning, China, in 2009. He is currently an associate professor in School of Artificial Intelligence, Guangxi University for Nationalities. His main research interests include rough set theory and information systems.

Gangqiang Zhang

Gangqiang Zhang received the M. Sc. degree in Software Engineering from Beihang University, Beijing, China, in 2006. He is currently an associate professor in School of Artificial Intelligence, Guangxi University for Nationalities. His main research interests include rough set theory, fuzzy set theory and information systems.

Ningxin Xie

Ningxin Xie received the M. Sc. degree in Computer from Guangxi University, Nanning, China, in 2001. He is currently a professor in School of Artificial Intelligence, Guangxi University for Nationalities. His main research interests include rough set theory, fuzzy set theory and information systems.