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. 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.
CitationNúñez, H.; González, L.; Angulo, C. A post-processing strategy for SVM learning from unbalanced data. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning". Bruges: 2011, p. 195-200.
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