基于双参照点灰色关联度的模糊支持向量机改进方法

Improved fuzzy support vector machine based on grey incidence degree with dual reference points

改进了模糊支持向量机隶属度函数设计方法。考虑样本分布不确定的问题,使用灰色关联度代替现有方法中的欧氏距离,定义了样本的平均灰色绝对关联度;针对噪声识别过程中支持向量对分类贡献被削弱的问题,提出了基于同类中心和异类中心双参照点的噪声判别方法;分析了模糊支持向量机求解对偶问题中参数与支持向量的对应关系,进而给出新的隶属度函数设置步骤。实验结果说明了方法的有效性及实用性。

We improve the membership setting method for the fuzzy support vector machine （FS- VM）. Given the distribution uncertainty of training sets, we replace the classical Euclidean distance with grey incidence degree, and define the mean grey absolute incidence degree of samples. To overcome the disadvantages in traditional methods and ensure a greater contribution of support vectors to the clas- sification results, we present a new approach for distinguishing noises based on the dual reference points of the same center and the different center. The relationship between the parameters in solving the FS- VM and the corresponding support vectors is analyzed, and the steps of setting the fuzzy membership are given. Experimental results demonstrate the effectiveness and practicability of this approach.