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ID 28413
file
A1005.pdf 391 KB
creator
Nguyen, Hien M.
Cooper, Eric W.
Kamei, Katsuari
NDC
Technology. Engineering
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 oversampling 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.
journal title
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
start page
24
end page
29
date of issued
2009-11
publisher
IEEE SMC Hiroshima Chapter
issn
1883-3977
language
eng
nii type
Conference Paper
HU type
Conference Papers
DCMI type
text
format
application/pdf
text version
publisher
rights
(c) Copyright by IEEE SMC Hiroshima Chapter.
relation url
department
Graduate School of Engineering