A post-processing strategy for SVM learning from unbalanced data
Haydemar Núñez, Luis Gonzalez-Abril and Cecilio Angulo
In: 19th European Symposium on Artificial Neural Networks, 27-29 Apr 2011, Bruges, Belgium.

Abstract

Standard learning algorithms may perform poorly when learning from unbalanced datasets. Based on the Fisher's discriminant analysis, a post-processing strategy is introduced to deal datasets with significant imbalance in the data distribution. Empirical results from experiments for a learned SVM model on twelve UCI datasets indicates that the proposed solution improves the original SVM, and they also improve those reported when using a z-SVM, in terms of g-mean and sensitivity.

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