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• W2000848210 abstract "This paper proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact models by selecting the adequate variables and adapting the number of fuzzy labels for each problem, improving the interpretability of the model. The experimental analysis is carried out over a wide range of highly imbalanced data-sets and uses the statistical tests suggested in the specialized literature." @default.
• W2000848210 created "2016-06-24" @default.
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• W2000848210 date "2012-05-17" @default.
• W2000848210 modified "2023-09-25" @default.
• W2000848210 title "FEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETS" @default.
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• W2000848210 doi "https://doi.org/10.1142/s0218488512500195" @default.
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