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

Muhammad Tahir, Josef Kittler, Fei Yan

Research output: Contribution to journalArticlepeer-review

142 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)3738-3750
JournalPattern Recognition
Volume45
Issue number10
Early online date13 Apr 2012
DOIs
Publication statusPublished - 1 Oct 2012
Externally publishedYes

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