中文摘要：

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．

英文摘要：

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.