A post-processing strategy for SVM learning from unbalanced data

2018-11-052018-11-052011Núñez Castro, H., González Abril, L. y Angulo Bahón, C. (2011). A post-processing strategy for SVM learning from unbalanced data. En 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (195-200), Bruges: Ciaco.978-2-87419-044-5https://hdl.handle.net/11441/79797Está en: https://upcommons.upc.edu/handle/2117/12531Standard 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. A new bias is defined, which reduces skew towards the minority class. 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.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/A post-processing strategy for SVM learning from unbalanced datainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess