基于样本之间紧密度的模糊支持向量机方法

针对传统支持向量机方法中存在对噪声或野值敏感的问题,提出了一种基于紧密度的模糊支持向量机方法.在确定样本的隶属度时,不仅考虑了样本与类中心之间的关系,还考虑了类中各个样本之间的关系.通过样本之间的紧密度来描述类中各个样本之间的关系,利用包围同一类中样本的最小球半径大小来度量样本之间的紧密度.样本的隶属度依据样本在球中的位置,按照不同的规律确定与基于样本与类中心之间关系构建的模糊支持向量机方法相比,该方法有利于将野值或含噪声样本与有效样本进行区分.实验结果表明,与传统支持向量机方法及基于样本与类中心之间关系的模糊支持向量机方法相比,基于紧密度的模糊支持向量机方法具有更好的抗噪性能及分类能力.

Fuzzy Support Vector Machine Based on Affinity Among Samples

Since SVM is very sensitive to outliers and noises in the training set, a fuzzy support vector machine algorithm based on affinity among samples is proposed in this paper. The fuzzy membership is defined by not only the relation between a sample and its cluster center, but also those among samples, which is described by the affinity among samples. A method defining the affinity among samples is considered using a sphere with minimum volume while containing the maximum of the samples. Then, the fuzzy membership is defined according to the position of samples in sphere space. Compared with the fuzzy support vector machine algorithm based on the relation between a sample and its cluster center, this method effectively distinguishes between the valid samples and the outliers or noises. Experimental results show that the fuzzy support vector machine based on the affinity among samples is more robust than the traditional support vector machine, and the fuzzy support vector machines based on the distance of a sample and its cluster center.