CORE: Core-based synthetic minority over-sampling and borderline majority under-sampling technique
Copyright © 2015 Inderscience Enterprises Ltd. Class imbalance learning has recently drawn considerable attention among researchers. In this area, a rare class is the class of primary interest from the aim of classification. Unfortunately, traditional machine learning algorithms fail to detect this...
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| Main Authors: | , |
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| Format: | Article |
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Inderscience Enterprises Ltd.
2015
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| Subjects: | |
| Online Access: | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84928784344&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38946 |
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| Institution: | Chiang Mai University |
| Summary: | Copyright © 2015 Inderscience Enterprises Ltd. Class imbalance learning has recently drawn considerable attention among researchers. In this area, a rare class is the class of primary interest from the aim of classification. Unfortunately, traditional machine learning algorithms fail to detect this class because a huge majority class overwhelms a tiny minority class. In this paper, we propose a new technique called CORE to handle the class imbalance problem. The objective of CORE is to strengthen the core of a minority class and weaken the risk of misclassified minority instances nearby the borderline of a majority class. These core and borderline regions are defined by the applicability of a safe level. As a result, a minority class is more crowed and dominant. The experiment shows that CORE can significantly improve the predictive performance of a minority class when its dataset is imbalance. |
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