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In the last years, multi-objective evolutionary algorithms have been used to learn or tune components of fuzzy systems from data. The suitability of such algorithms for this task is due to the possibility of balancing the conflicting objectives of accuracy and interpretability of the resulting model. In a previous work, a method to learn fuzzy classification rules from imbalanced datasets using multi-objective...
In this paper, we propose a multiobjective genetic method to learn fuzzy rules and optimize fuzzy sets in Fuzzy Rule Based Classification Systems (FRBCSs) aiming at finding a balance between the accuracy and interpretability objectives. The proposed method comprises three sequential stages: Data Base definition, Rule Base Learning and Data Base Optimization. The two objectives considered are related...
Fuzzy classification systems have been widely researched in the literature. Genetic fuzzy systems combine the power of the global search of genetic algorithms with fuzzy systems to provide accurate and interpretable rule-based systems. In this paper, we present a new approach for the genetic generation of fuzzy systems. The novelty of our proposal, named FCA-Based method, is a hybrid combination of...
This paper aims at investigating the advantages of using an interval type-2 fuzzy system for classification problems. An evolutionary architecture was proposed to generate the rule base and to optimize the membership functions of a type-2 Fuzzy Classification System The proposed architecture is composed of three stages. In the first stage of the architecture, a Genetic Algorithm generates the rule...
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