基于识别关键样本点的非平衡数据核SVM算法

doi:10.16451/j.cnki.issn1003-6059.201906009

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Abstract

Under-sampling is often employed in imbalanced data processing. However, the characteristics of support vector machine(SVM) are seldom taken into account in the existing under-sampling methods,and the problem of losing some key information of the majority class is caused by the sampling in the original space. To solve these problems, a kernel SVM algorithm based on identifying key samples for imbalanced data(IK-KSVM) is proposed in this paper. Firstly, the majority class is divided effectively based on the initial hyperplane. Then, kernel heterogeneous nearest neighbor sampling is conducted on each partition to obtain the key samples of the majority class in the high-dimensional space. Finally, the final SVM classifier is trained by the key samples and the minority class samples. Experiments on several datasets show that IK-KSVM is feasible and effective and its advantages are evident while the imbalance degree of the dataset is higher than 10∶1.

Key words： Imbalanced Data Kernel Support Vector Machine Partition Under-Sampling

Received: 05 March 2019

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About author:: (GUO Ting, master student. Her research interests include data mining and machine learning.)(Wang Jie, Ph.D. candidate. His research interests include data mining and machine learning.)(LIU Quanming, Ph.D., associate profe-ssor. His research interests include cloud storage and cloud security, network behavior analysis and data mining.)(LIANG Jiye(Corresponding author), Ph.D., professor. His research interests include granu-lar computing, data mining and machine learning.)

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	GUO Ting
	WANG Jie
	LIU Quanming
	LIANG Jiye

Cite this article:

GUO Ting,WANG Jie,LIU Quanming等. Kernel SVM Algorithm Based on Identifying Key Samples for Imbalanced Data[J]. , 2019, 32(6): 569-576.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201906009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I6/569

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