Some relationships between fuzzy and random set-based classifiers and models

DOI10.1016/S0888-613X(01)00063-9zbMath1015.68181WikidataQ62608500 ScholiaQ62608500MaRDI QIDQ1347873

Jorge Casillas, Oscar Cordón, Luciano Sánchez, María José del Jesus

Publication date: 15 May 2002

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

zbMATH Keywords

fuzzy modelsclassifiersprobabilistic rule-based models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

A fast genetic method for inducting descriptive fuzzy models. ⋮ A new probabilistic fuzzy model: Fuzzification-Maximization (FM) approach ⋮ Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms ⋮ Towards an (even more) natural probabilistic interpretation of fuzzy transforms (and of fuzzy modeling) ⋮ Genetic learning of fuzzy rules based on low quality data

Cites Work

• The concept of a linguistic variable and its application to approximate reasoning. I
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Fuzzy identification of systems and its applications to modeling and control
• Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
• Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
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