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Feature selection for bankruptcy prediction : a multi-objective optimization ap...
Mendes, F.; Duarte, J.; Vieira, Armando; Gaspar-Cunha, A.; Ribeiro, Bernardete; Ribeiro, André M. S.; Neves, João CarvalhoIn this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – is fully analyzed using two classifiers: Support Vector Machines and Logistic Function. A...
Multi-objective evolutionary algorithms for feature selection : application in ...
Gaspar-Cunha, A.; Mendes, F.; Duarte, J.; Vieira, Armando; Ribeiro, Bernardete; Ribeiro, André M. S.; Neves, João CarvalhoA Multi-Objective Evolutionary Algorithm (MOEA) was adapted in order to deal with problems of feature selection in datamining. The aim is to maximize the accuracy of the classifier and/or to minimize the errors produced while minimizing the number of features necessary. A Support Vector Machines (SVM) classifier was adopted. Simultaneously, the parameters required by the classifier were also optimized. The vali...
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