Inverse random under sampling for class imbalance problem and its application to multi-label classification

Tahir, Muhammad, Kittler, Josef and Yan, Fei (2012) Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recognition, 45 (10). pp. 3738-3750. ISSN 0031-3203

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Class imbalance problem, multi-label classification, inverse random under sampling
Subjects:	G700 Artificial Intelligence G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Muhammad Tahir
Date Deposited:	08 May 2012 09:30
Last Modified:	10 Dec 2019 12:46
URI:	http://nrl.northumbria.ac.uk/id/eprint/6803

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