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Real world data sets are regularly unbalanced, which is a crucial problem in Machine Learning, it causes a low accuracy in most classification techniques. SVM have reported excellent generalization capability in recent years; however, working with unbalanced data sets have poor performance due to the retrieved hyperplane is biased towards the majority class. This article presents a new algorithm capable of generating artificial points within the minority class and on the border between classes from the positive Support Vectors, these new points are added to the training data set in order to reduce the imbalance between classes, improving the performance of SVM in the majority of tests. 93 Research in Computing Science 116 (2016) pp. 93–105; rec. 2016-03-17; acc. 2016-05-16

Mejora del desempeño de SVM usando un GA y la creación de puntos artificiales para conjuntos de datos no balanceados