面向非平衡数据处理的样例惩罚支持向量机

支持向量机在处理非平衡数据集时常常不能取得良好的效果,因为其分类性能只考虑了总体分类精度,而忽略了不同类别样例之间的精度权衡.本文提出了一种基于样例分布的样例惩罚支持向量机,可以针对每一个样例根据其相应的分布特性选取惩罚以获得高敏感度的分类面.实验表明,该模型比标准支持向量机在非平衡数据上具有更好的性能.

Support Vector Machines with Example Dependent Costs for Dealing with Imbalanced Data

Standard SVM often performs poorly on imbalanced datasets for the reason that SVM ignores the tradeoff of the precision between different classes while just takes the overall classification accuracy into account.A new example dependent costs SVM method was proposed,from which we can get more sensitive hyperplane by selecting penalty for every sample according to its corresponding distribution.Experimental results show that this method can efficiently and effectively improve the performance on imbalanced datasets,better than the standard SVM method for comparison.