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 language | English |
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Pages (from-to) | 3738-3750 |
Journal | Pattern Recognition |
Volume | 45 |
Issue number | 10 |
Early online date | 13 Apr 2012 |
DOIs | |
Publication status | Published - 1 Oct 2012 |
Externally published | Yes |
Keywords
- Class imbalance problem
- multi-label classification
- inverse random under sampling