Abstract
An optimization methodology based on the use of Multi-Objective Evolutionary Algorithms (MOEA) in order to deal with problems of feature selection in data mining was proposed. For that purpose a Support Vector Machines (SVM) classifier was adopted. The aim being to select the best features and optimize the classifier parameters simultaneously while minimizing the number of features necessary and maximize the accuracy of the classifier and/or minimize the errors obtained. The validity of the methodology proposed was tested in a problem of cardiac Single Proton Emission Computed Tomography (SPECT). The results obtained allow one to conclude 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 simultaneously.
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Gaspar-Cunha, A. (2010). Feature Selection Using Multi-Objective Evolutionary Algorithms: Application to Cardiac SPECT Diagnosis. In: Rocha, M.P., Riverola, F.F., Shatkay, H., Corchado, J.M. (eds) Advances in Bioinformatics. Advances in Intelligent and Soft Computing, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13214-8_11
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DOI: https://doi.org/10.1007/978-3-642-13214-8_11
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