ID | 19617 |
Eprint ID | 19617
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FullText URL | |
Author |
Nguyen Hien M.
Cooper Eric W.
Kamei Katsuari
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Abstract | Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through over-sampling at the borderline between the minority class and the majority class of the data set. A Support Vector Machines (SVMs) classifier then is trained to predict new unknown instances. Compared to other over-sampling methods, the proposed method focuses only on the minority class instances lying around the borderline due to the fact that this area is most crucial for establishing the decision boundary. Furthermore, new instances will be generated in such a manner that minority class area will be expanded further toward the side of the majority class at the places where there appear few majority class instances. Experimental results show that the proposed method can achieve better performance than some other over-sampling methods, especially with data sets having low degree of overlap due to its ability of expanding minority class area in such cases.
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Published Date | 2009-11-10
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Publication Title |
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
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Volume | volume2009
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Issue | issue1
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Publisher | IEEE SMC Hiroshima Chapter
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Start Page | 24
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End Page | 29
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ISSN | 1883-3977
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NCID | BB00577064
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Content Type |
Conference Paper
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language |
English
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Copyright Holders | IEEE SMC Hiroshima Chapter
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Event Title | 5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
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Event Location | 東広島市
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Event Location Alternative | Higashi-Hiroshima City
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File Version | publisher
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Refereed |
True
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Eprints Journal Name | IWCIA
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