Random Walk-steered Majority Undersampling

• URL: http://arxiv.org/abs/2109.12423v1
• Date: Sat, 25 Sep 2021 19:07:41 GMT
• Title: Random Walk-steered Majority Undersampling
• Authors: Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee
• Abstract summary: We propose Random Walk-steered Majority Undersampling (RWMaU) RWMaU undersamples the majority points of a class imbalanced dataset in order to balance the classes. Empirical evaluation on 21 datasets and 3 classifiers demonstrate substantial improvement in performance of RWMaU over the competing methods.
• Score: 10.227026799075215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes. Rather than marking the majority points which belong to the neighborhood of a few minority points, we are interested to perceive the closeness of the majority points to the minority class. Random walk, a powerful tool for perceiving the proximities of connected points in a graph, is used to identify the majority points which lie close to the minority class of a class-imbalanced dataset. The visit frequencies and the order of visits of the majority points in the walks enable us to perceive an overall closeness of the majority points to the minority class. The ones lying close to the minority class are subsequently undersampled. Empirical evaluation on 21 datasets and 3 classifiers demonstrate substantial improvement in performance of RWMaU over the competing methods.

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