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一种面向不平衡数据集的核Fisher线性判别分析方法 被引量：6

A Kernel Fisher Linear Discriminant Analysis Approach Aiming at Imbalanced Data Set
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摘要 实际应用中,很多分类问题是面向不平衡数据的分类,而不平衡数据集会导致许多分类器的性能下降.文中介绍核Fisher线性判别分析的分类机制,分析不平衡数据导致核Fisher线性判别分析失效的原因,进而提出一种加权核Fisher线性判别分析方法.该方法通过调整两类样本的核协方差矩阵对核类内离散度矩阵的贡献,可克服不平衡数据对分类性能的影响.为进一步测试该方法,对UCI数据集进行实验测试,实验结果表明该方法可有效改进分类器的分类性能. 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.
作者 尹军梅 杨明 万建武
机构地区 南京师范大学计算机科学与技术学院 江苏省信息安全保密技术工程研究中心
出处 《模式识别与人工智能》 EI CSCD 北大核心 2010年第3期414-420,共7页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金(No.60873176) 江苏省自然科学基金(No.BK2008430)资助项目
关键词 不平衡数据集 核Fisher线性判别分析(KFDA) 过抽样 欠抽样 Imbalanced Data Set, Kernel Fisher Linear Discriminant Analysis （KFDA）, Over-Sampling,Under-Sampling
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献20

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二级参考文献30

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共引文献72

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引证文献6

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模式识别与人工智能
2010年 第3期

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