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

Tahir, MA, Kittler, J and Yan, F (2012) Inverse random under sampling for class imbalance problem and its application to multi-label classification Pattern Recognition, 45 (10). pp. 3738-3750.

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Abstract

In this paper, a novel inverse random undersampling (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
Divisions :	Surrey research (other units)
Authors :	Tahir, MA, Kittler, J and Yan, F
Date :	2012
DOI :	10.1016/j.patcog.2012.03.014
Depositing User :	Symplectic Elements
Date Deposited :	28 Mar 2017 14:11
Last Modified :	24 Jan 2020 11:51
URI:	http://epubs.surrey.ac.uk/id/eprint/733256

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