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. 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.