Fuzzy entropies for class-specific and classification-based attribute reducts in three-way probabilistic rough set models
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Summary
This paper constructs monotonic measures based on fuzzy entropies and develops algorithms for finding the two types of attribute reducts based on addition-deletion method or deletion method and shows that class-specific attribute reduCTs provide a more effective way of attribute reduction with respect to a particular decision class compared with classification-based attribute reducci.
- Type
- article
- Published
- 2020-08-17
- Cited by
- 19
- References
- 74
- OpenAlex
- https://openalex.org/W3056677940
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:225366510
Keywords
Rough set, Probabilistic logic, Mathematics, Class (philosophy), Data mining
References
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- The WEKA data mining software: an update
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- Feature selection based on min-redundancy and max-consistency
- Three-way improved neighborhood entropies based on three-level granular structures
- RETRACTED: Class-specific attribute reducts based on neighborhood rough sets
- Fuzzy information-theoretic feature selection via relevance, redundancy, and complementarity criteria
- Three-way multi-attribute decision-making with multiple decision makers in heterogeneous incomplete decision systems
- A class-specific feature selection and classification approach using neighborhood rough set and K-nearest neighbor theories
- Class-specific feature selection via maximal dynamic correlation change and minimal redundancy
- Hypergraph-based attribute reduction of formal contexts in rough sets
- Double-local conditional probability based fast calculation method for approximation regions of local rough sets
- Multi-scale decision systems with test cost and applications to three-way multi-attribute decision-making
- Tri-level attribute reduction based on neighborhood rough sets
- Three-way class-specific attribute reducts based on three-way weighted combination-entropies
- Feature selection via Class-specific Approximate Markov Blanket and Rough Set-based Mapping
- Improved attribute reductions based on combined measures from dependency-roughness-entropy fusion and classification-class-level integration
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