Extract minimum positive and maximum negative features for imbalanced binary classification

In an imbalanced dataset, the positive and negative classes can be quite different in both size and distribution. This degrades the performance of many feature extraction methods and classifiers. This paper proposes a method for extracting minimum positive and maximum negative features (in terms of absolute value) for imbalanced binary classification. This paper develops two models to yield the feature extractors. Model 1 first generates a set of candidate extractors that can minimize the positive features to be zero, and then chooses the ones among these candidates that can maximize the negative features. Model 2 first generates a set of candidate extractors that can maximize the negative features, and then chooses the ones that can minimize the positive features. Compared with the traditional feature extraction methods and classifiers, the proposed models are less likely affected by the imbalance of the dataset. Experimental results show that these models can perform well when the positive class and negative class are imbalanced in both size and distribution.