Fisher Linear Discriminant Model with Class Imbalance
Zhengding Qiu · Journal of Beijing Jiaotong University · 2006
As the majority of classification methods previously designed usually assume that their training sets are well-balanced,they have to be affected by class imbalance in which examples in training data belonging to one class heavily outnumber the examples in the other class.This paper demonstrates that,when the two sample covariance matrices are not identical,class imbalance has a negative effect on the performance of Fisher linear discriminant(FLD).A weighted FLD(WFLD) is proposed for reducing the negative effects of the class imbalance.Using area under the ROC curve as performance measarement,eight UCI imbalanced data sets are tested to show WFLD's effectiveness.