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ID 19617
Eprint ID
19617
フルテキストURL
著者
Nguyen Hien M. Ritsumeikan University
Cooper Eric W. Ritsumeikan University
Kamei Katsuari Ritsumeikan University
抄録
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.
発行日
2009-11-10
出版物タイトル
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
2009巻
1号
出版者
IEEE SMC Hiroshima Chapter
開始ページ
24
終了ページ
29
ISSN
1883-3977
NCID
BB00577064
資料タイプ
会議発表論文
言語
English
著作権者
IEEE SMC Hiroshima Chapter
イベント
5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
イベント地
東広島市
イベント地の別言語
Higashi-Hiroshima City
論文のバージョン
publisher
査読
有り
Eprints Journal Name
IWCIA