一种核模糊分类器的规则生成方法

A Method of Generating Rules with a Kernel Fuzzy Classifier

提出一种基于核函数的模糊分类器的模糊规则产生方法.首先借鉴支持向量机(SVM)的思想,选用适当的核函数,将初始的样本空间映射为高维的特征空间,使得样本在高维特征空间的分布比在原来空间的分布简单可分.然后,用一种动态聚类方法,在高维特征空间将同一类的训练样本分成簇,求出该簇的支持向量.对于每簇建立一个模糊规则,隶属函数采用超椭圆体函数.最后,利用遗传算法对规则进行优化调整.用两个典型的数据集来评测本文所提方法构成的分类器,结果表明这种分类器学习时间短,分类精度较高,分类速度较快.

A method of generating rules with kernel fuzzy classifier is introduced in this paper. This method Selects appropriate kernel function by the principle of SVM. Firstly, the initial sample space is mapped into a high dimensional feature space in order to simplify and separate the samples. Then in the feature space, the dynamic clustering arithmetic dynamically separates the training samples into different clusters and finds out the support vectors of each cluster. For each cluster, a fuzzy rule is defined with ellipsoidal regions. Finally, the rules are tuned by Genetic Algorithms. This method is evaluated by two typical data sets. For the classifier with this method, the learning time is short, and the accuracy and the speed of classification are relatively high.