IRUSRT: A novel imbalanced learning technique by combining inverse random under sampling and random tree

In this article, a novel technique IRUSRT (inverse random under sampling and random tree) by combining inverse random under sampling and random tree is proposed to implement imbalanced learning. The main idea is to severely under sample the majority class thus creating multiple distinct training sets. With each training set, a random tree is trained to separate the minority class from the majority class. By combining these random trees through fusion, a composite classifier is constructed. The experimental analysis on 23 real-world datasets assessed over area under the ROC curve (AUC), F-measure, and G-mean indicates that IRUSRT performs significantly better when compared with many existing class imbalance learning methods.