一种面向不平衡数据集的核Fisher线性判别分析方法

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Abstract In practical real applications lots of classification questions are aiming at imbalanced data sets, while these unbalanced data will lead to the descending of the classification performance of many classifiers. In this paper the classification mechanism based on kernel fisher linear discriminant analysis (KFDA) is introduced, and then the reasons that the unbalanced data cause KFDA to turn ineffective is analyzed. Therefore, a weighted kernel fisher linear discriminant analysis (WKFDA) method is proposed. The method balances the contributions from kernel covariance matrices of two classes of sample to the kernel within-class scatter matrix and can constrain the influence of unbalanced data on classification performance. The experiments on 7 UCI datasets are performed to further test the performance of our algorithm. The experimental results show that the developed approach can effectively improve the classification performance of the proposed classifier.

Key words： Imbalanced Data Set Kernel Fisher Linear Discriminant Analysis (KFDA) Over-Sampling Under-Sampling

Received: 14 July 2008

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	Articles by authors
	YIN Jun-Mei
	YANG Ming
	WAN Jian-Wu

Cite this article:

YIN Jun-Mei,YANG Ming,WAN Jian-Wu. A Kernel Fisher Linear Discriminant Analysis Approach Aiming at Imbalanced Data Set[J]. , 2010, 23(3): 414-420.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2010/V23/I3/414

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