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| Attribute Reduction of Optimistic Multi-granularity Fuzzy Decision Rough Sets Based on Fuzzy Self-Information |
| GAI Cancan1, MA Jianmin1 |
| 1. School of Sciences, Chang'an University, Xi'an 710064 |
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Abstract In current attribute reduction methods for multi-granularity fuzzy rough sets, the fuzzy similarity relationship within attribute sets is often defined using a fixed threshold. However, in fuzzy decision systems, different thresholds are required for different attributes, since each attribute has a different value range. To address this issue, the attribute reduction of optimistic multi-granularity fuzzy decision rough sets based on fuzzy self-information is proposed in this paper. First, within the fuzzy decision information system, the threshold for each attribute is introduced based on the quantile to construct an adaptive-threshold-based fuzzy similarity relation. An optimistic multi-granularity fuzzy decision rough set model for a family of attribute subsets is then established. Second, fuzzy self-information is proposed by utilizing the lower and upper approximations of optimistic multi-granularity fuzzy decision rough set model and boundary domain information. Furthermore, an attribute significance measure is introduced, and an attribute reduction algorithm based on fuzzy self-information is developed. The experimental results on ten datasets demonstrate that the proposed algorithm effectively reduces the dimensionality of the original attribute set and improves the classification accuracy.
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Received: 21 August 2025
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| Fund:National Natural Science Foundation of China(No.12471480), 2022 National Key Research and Development Program of Ministry of Science and Technology of China(No.2022YFC3303100), Natural Science Basic Research Plan in Shaanxi Province(No.2022JM-032) |
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Corresponding Authors:
MA Jianmi, Ph.D., professor. Her research interests include rough sets, granular computing, concept lattice, three-way decisions, statistical learning methods and data analysis.
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| About author:: GAI Cancan, Master student.Her research interests include rough sets and granular computing. |
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2025, Vol. 38 
