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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
Authors :
AuthorsEmailORCID
Tahir, MAUNSPECIFIEDUNSPECIFIED
Kittler, JUNSPECIFIEDUNSPECIFIED
Yan, FUNSPECIFIEDUNSPECIFIED
Date : 2012
Identification Number : https://doi.org/10.1016/j.patcog.2012.03.014
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:11
Last Modified : 28 Mar 2017 14:11
URI: http://epubs.surrey.ac.uk/id/eprint/733256

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