In this paper, a novel inverse random under sampling (IRUS) method is proposed for the class imbalance problem. The main idea is to severely under sample the majority class thus creating a large number of distinct training sets. For each training set we then find a decision boundary which separates the minority class from the majority class. By combining the multiple designs through fusion, we construct a composite boundary between the majority class and the minority class. The proposed methodology is applied on 22 UCI data sets and experimental results indicate a significant increase in performance when compared with many existing class-imbalance learning methods. We also present promising results for multi-label classification, a challenging research problem in many modern applications such as music, text and image categorization. © 2012 Elsevier Ltd.
CITATION STYLE
Tahir, M. A., Kittler, J., & Yan, F. (2012). Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recognition, 45(10), 3738–3750. https://doi.org/10.1016/j.patcog.2012.03.014
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