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Attribute reduction in an incomplete categorical decision information system based on fuzzy rough sets

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Abstract

Categorical data is an important class of data in machine learning. Information system based on categorical data is called a categorical information system (CIS), a CIS with missing values is known as an incomplete categorical information system (ICIS) and an ICIS with decision attributes is said to be an incomplete categorical decision information system (ICDIS). Attribute selection is an important subject in rough set theory. This paper investigates attribute reduction in an ICDIS based on fuzzy rough sets. To depict the similarity for incomplete categorical data, fuzzy symmetry relations in an ICDIS are first introduced. Then, some attribute-evaluation functions, such fuzzy positive regions, dependency function and attribute importance functions are given. Next, the fuzzy-rough iterative computation model for an ICDIS is presented, and an attribute reduction algorithm in an ICDIS based on fuzzy rough sets is given. Finally, experiments are carried out as so to evaluate the performance of the proposed algorithm, and Friedman test and Bonferroni-Dunn test in statistics are conducted. The experimental results indicate that the proposed algorithm is more effective than some existing algorithms.

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Acknowledgements

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), Natural Science Foundation of Guangxi (AD19245102, 2020GXNSFAA159155, 2018GXNSFDA294003), Key Laborabory of Software Engineering in Guangxi University for Nationalities (2021-18XJSY-03) and Special Scientific Research Project of Young Innovative Talents in Guangxi (2019AC20052).

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Authors and Affiliations

  1. Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Jiali He

  2. School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, Guangxi, People’s Republic of China

    Liangdong Qu

  3. Institute of Artificial Intelligence, School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, Sichuan, People’s Republic of China

    Zhihong Wang

  4. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, 363000, Fujian, People’s Republic of China

    Yiying Chen

  5. School of Mathematics and Information Science, Guangxi University, Nanning, 530004, Guangxi, People’s Republic of China

    Damei Luo

  6. Center for Fundamental Science, and Research Center for Nonlinear Analysis and Optimization, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan

    Ching-Feng Wen

  7. Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, 80708, Taiwan

    Ching-Feng Wen

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  1. Jiali He
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  2. Liangdong Qu
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  3. Zhihong Wang
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  4. Yiying Chen
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  5. Damei Luo
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  6. Ching-Feng Wen
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Correspondence to Liangdong Qu or Ching-Feng Wen.

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He, J., Qu, L., Wang, Z. et al. Attribute reduction in an incomplete categorical decision information system based on fuzzy rough sets. Artif Intell Rev 55, 5313–5348 (2022). https://doi.org/10.1007/s10462-021-10117-w

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