Feature selection for a set-valued decision information system based on fuzzy rough iterative computation model

Abstract

To deal with set-valued data, one usually exploits the information value similarity, which is fed back to the object set. Alternatively, it is feasible but scarcely considered to handle set-valued data from the perspective of the information-value similarity which is fed back to the feature set. Based on this perspective, this paper studies feature selection for a set-valued decision information system (SVDIS) by means of fuzzy rough iterative computation model. In order to describe the similarity between objects, the fuzzy symmetric relations in an SVDIS are first defined, and a variable parameter to control the similarity is introduced. Then, new fuzzy rough set model for set valued data is proposed, and this model employs the iterative computation strategy to define fuzzy rough approximations and dependency functions. Next, fuzzy rough iterative computation model is established by using the iteration of fuzzy positive region and fuzzy dependency. Moreover, fuzzy rough iterative computation model is applied to the feature selection for set-valued data, and the corresponding algorithm is given. Finally, experiments are carried out to evaluate the performance of the given algorithm. The experimental results indicate that the given algorithm is more effective than some existing algorithms.

Keywords:

• SVDIS
• fuzzy rough set
• evaluation functions
• dependency function
• feature selection
• fuzzy rough iterative computation model

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.

Disclosure statement

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

Additional information

Funding

This study is supported by grants from 2021 High-Level Talent Project of Yulin Normal University [grant number G2021ZK05].

Notes on contributors

Yichun Peng

Yichun Peng received the Ph.D. degree in Environmental Science from University of Chinese Academy of Sciences, Beijing, China, in 2015. He is currently an Associate Professor with the School of Computer Science and Engineering, Yulin Normal University, Yulin, China. His current research interests include geographic information systems and remote sensing, neural networks and rough set theory.