NDLI: A quasi-linear SVM combined with assembled SMOTE for imbalanced data classification

Content Provider	IEEE Xplore Digital Library
Author	Bo Zhou Cheng Yang Haixiang Guo Jinglu Hu
Copyright Year	2013
Description	Author affiliation: Sch. of Econ. & Manage., China Univ. of Geosci., Wuhan, China (Haixiang Guo) \|\| Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan (Bo Zhou; Cheng Yang; Jinglu Hu)
Abstract	This paper focuses on imbalanced dataset classification problem by using SVM and oversampling method. Traditional oversampling method increases the occurrence of over-lapping between classes, which leads to poor generalization of SVM classification. To solve this problem this paper proposes a combined method of quasi-linear SVM and assembled SMOTE. The quasi-linear SVM is an SVM with quasi-linear kernel function. It realizes an approximate nonlinear separation boundary by mulit-local linear boundaries with interpolation. The assembled SMOTE implements oversampling with considering of the data distribution information and avoids occurrence of overlapping between classes. Firstly, a partition method based on Minimal Spanning Tree is proposed to obtain local linear partitions, each of which can be separated with one linear separation boundary. Secondly, using the information of local linear partitions, the assembled SMOTE generates synthetic minority class samples. Finally, the quasi-linear SVM realizes a classification of oversampled datasets in the same way as a standard SVM by using a composite quasi-linear kernel function. Experiment results on artificial data and benchmark datasets show that the proposed method is effective and improves classification performances.
Starting Page	1
Ending Page	7
File Size	428059
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6707035
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Merging Standards Sociology Statistics Interpolation
Content Type	Text
Resource Type	Article

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