Random Walk-steered Majority Undersampling

Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee

Abstract: This paper proposes Random Walk-steered Majority Undersampling (RWMaU), an undersampling approach to address the class imbalance problem for binary classifiers. RWMaU is focused to find the majority points which lie at the overlapped region of the minority and the majority classes. Such points meddle with the learning and detection of the minority points. RWMaU uses random walks to mark the majority points satisfying the above characteristic in a non-parametric fashion. For each majority point, a proximity score is calculated on the basis of - their visit frequencies and the order of visits of the majority points in the random walks. This score is used to perceive the closeness of the majority class points to the minority class. The majority points lying close to the minority class are subsequently undersampled. Empirical evaluations on 21 datasets using 3 classifiers demonstrate substantial improvement in performance of RWMaU over existing methods for addressing class imbalance and show that it is an efficient and effective way to address class imbalance in binary classification problems.