紧密类超带模糊支持向量机

Affinity Class-hyperparallel Fuzzy Support Vector Machine

提出一种紧密类超带模糊支持向量机(Affinity Class-Hyperparallel Fuzzy Support Vector Machine,ACHFS-VM),其以获得较好的抗噪性和泛化能力。该方法在摒弃样本集球形分布假设的同时,纳入对样本紧密度的考量,用类内超平面取代类中心,通过二次规划的方法在特征空间中寻找最小类超带,以其带宽表征样本紧密度,构造S型隶属度函数。基于UCI数据集的仿真结果表明该方法较同类算法具有更好的抗噪和分类性能。

An Affinity Class-Hyperparallel Fuzzy Support Vector Machine was proposed to get better classification result.This method not only takes the advantage of the affinity,but also abandons the estimation that the samples obey spherical-shape distribution.Instead of the cluster center,a hyperplane within the class is used to find a hyperparallel with the minimum distance while containing the maximum samples by the way of quadratic programming.The membership is achieved through a new S-function based on the distance of the hyperparallel which reflects the affinity of the samples.The simulation on UCI shows that the ACHFSVM is more robust and has better classification accuracy.