Abstract: The generalization of Support Vector Machines (SVM) will decline when the training data sets get imbalanced distribution. A modification method of the optimal hyperplane based on average divergence ratio according to Fisher within-class scatter was proposed to solve the problem. The normal vector of the optimal hyperplane was got after SVM training. The Fisher within-class scatter was introduced to evaluate the distribution of the two classes. On this basis, the optimal hyperplane was modified by the ratio of the average distribution scatter that was obtained according to the number of samples. The experimental results on benchmarks data sets show that the proposed method improves the classification accuracy of the class with less training data, so as to improve the SVM's generalization.

Key words: Support Vector Machine (SVM), imbalanced data, modification, Fisher within-class scatter

摘要： 针对支持向量机(SVM)训练不平衡样本数据产生最优分类面的偏移会降低分类模型泛化性的问题,提出一种基于Fisher类内散度平均分布比的分类面修正方法。对样本数据进行SVM训练后获得分类面的法向量;通过计算两类样本在该法向量方向上的Fisher类内散度来评价这两类样本的分布情况;依据类内散度综合考虑样本个数所得到的平均分布比重新修正最优分类面的位置。在benchmarks数据集上的实验结果说明该方法能够提高SVM分类模型在处理不均衡数据集时对于少数类的识别率,从而有助于提高模型的泛化性。

关键词: 支持向量机, 不平衡数据, 修正, Fisher类内散度