Abstract
A Multi-Objective Evolutionary Algorithm (MOEA) was adapted in order to deal with problems of feature selection in data-mining. 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 validity of the methodology proposed was tested in the problem of bankruptcy prediction using a database containing financial statements of 1200 medium sized private French companies. The results produced shown that MOEA is an efficient feature selection approach and the best results were obtained when the accuracy, the errors and the classifiers parameters are optimized.
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Gaspar-Cunha, A. et al. (2010). Multi-Objective Evolutionary Algorithms for Feature Selection: Application in Bankruptcy Prediction. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_33
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DOI: https://doi.org/10.1007/978-3-642-17298-4_33
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