基于简约凸壳的一类模糊支持向量机

One-Class Fuzzy Support Vector Machine Based on Reduced Convex Hull

为解决传统一类支持向量机对噪声数据敏感和不适用于大规模分类等问题,提出了用于大规模噪声环境的基于简约凸壳的一类模糊支持向量机(OC-FSVM-RCH).OC-FSVM-RCH根据简约凸壳的定义在核空间得到代表正常类数据几何特征的样本,然后基于改进的模糊支持向量域描述算法,使得正常类数据包含在最小超球内,异常数据与超球间隔最大化.OC-FSVM-RCH剔除正常类数据轮廓边缘处的噪声,同时对数据内部的噪声不敏感.实验结果表明了所提算法在性能和训练时间上取得了良好的效果.

The traditional one-class support vector machines are sensitive to noise data and not suitable for large-scale classification.In order to solve the problem,a novel one-class fuzzy support vector machine based on reduced convex hull called OC-FSVM-RCH is proposed for large-scale noise data classification.According to the reduced convex hull,OC-FSVM-RCH obtains the samples representing the geometric characteristics of normal class data in the kernel space.Then OC-FSVM-RCH improves the fuzzy support vector domain description algorithm,in which normal class data is enclosed in the smallest hypersphere,and the margin between abnormal class data and hypersphere is maximized.OC-FSVM-RCH can eliminate the noise at the edge of normal data contour and is insensitive to the noise inside the normal data.Experimental results show that the proposed algorithm achieves good results in terms of performance and training time.