Abstract
This paper deals with the problem of training a discriminative classifier when the data sets are imbalanced. More specifically, this work is concerned with the problem of classify a sample as belonging, or not, to a Target Class (TC), when the number of examples from the “Non-Target Class” (NTC) is much higher than those of the TC. The effectiveness of the heuristic method called Non Target Incremental Learning (NTIL) in the task of extracting, from the pool of NTC representatives, the most discriminant training subset with regard to the TC, has been proved when an Artificial Neural Network is used as classifier (ISMIS 2003). In this paper the effectiveness of this method is also shown for Support Vector Machines.
This work has been supported by Ministerio de Ciencia y Tecnología, Spain, under Project TIC2003-08382-C05-03.
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Vivaracho, C.E. (2006). Improving SVM Training by Means of NTIL When the Data Sets Are Imbalanced. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_14
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DOI: https://doi.org/10.1007/11875604_14
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