PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8610
Deposited By:Cecilio Angulo
Deposited On:13 February 2012