Fuzzy similarity and entropy (FSAE) feature selection revisited by using intra-class entropy and a normalized scaling factor

DOI10.1007/978-3-030-93699-0_4OpenAlexW4214821944MaRDI QIDQ2100516

Christoph Lohrmann, Pasi Luukka

Publication date: 22 November 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-93699-0_4

zbMATH Keywords

classificationsimilarityfeature selectionfuzzy entropyintra-class

Mathematics Subject Classification ID

Computer science (68-XX)

Uses Software

• ReliefF

Cites Work

• New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle
• Selection of relevant features and examples in machine learning
• Fuzzy data analysis
• Learning Boolean concepts in the presence of many irrelevant features
• Evaluating feature selection methods for learning in data mining applications.
• Theoretical and empirical analysis of ReliefF and RReliefF
• Support-vector networks
• Invariant optimal feature selection: A distance discriminant and feature ranking based solution
• Nearest neighbor pattern classification
• A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory
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