基于边界向量提取的模糊支持向量机方法

Fuzzy Support Vector Machine Method Based on Border Vector Extraction

针对支持向量机对训练样本中的噪声和孤立点特别敏感的问题,提出一种基于边界向量提取的模糊支持向量机方法.在特征空间中寻找能够分别包住两类样本点的两个最小超球,并选择可能成为支持向量的边界向量作为新样本,减少参与训练的样本数目,提高训练速度.样本的隶属度根据边界样本和噪声点与所在超球球心的距离分别确定,既减弱孤立点和噪声的影响,又增强支持向量对支持向量机分类的作用.实验结果表明,与传统的支持向量机方法和基于样本与类中心之间关系的模糊支持向量机相比,本文方法具有更快的学习速度和更好的泛化能力.

A fuzzy support vector machine （SVM） based on border vector extraction is presented. It overcomes the disadvantage of the sensitivity to noises and the outliers in the training samples. Border vectors, which are possible support vectors, are selected as new samples to train SVMs. The number of training samples is reduced and thus the training speed is improved. The fuzzy membership is defined according to the distance from border vectors and outliers to their hypersphere centers. Consequently the effect of noises and outliers is weakened and support vectors are improved to design a classifier. Experimental results show that by the proposed method the machine is less sensitive to noises and outliers than by the traditional SVMs and the fuzzy SVMs based on the distance between a sample and its cluster center. Furthermore, the proposed method has better generalization ability and higher learning speed than the others.