Text classifier based on fuzzy support vector machine and decision tree
基于模糊支持向量机与决策树的文本分类器

For determining the membership function in text classification with fuzzy support vector machine, a construction approach of text classifier based on fuzzy support vector machine and decision tree was proposed. The relationship between the sample and its cluster center was considered and the tangent sphere was constructed by the hyperplane that contained the support vectors and paralleled the classification hyperplane in traditional support vector machine, so to further determine the relation of all samples in the class. The membership of one sample to a class could be computed by the location of the sample in the sphere, so the efficient samples, noises and outliers could be distinguished rationally. Integrating the decision tree method, the classification of multi-classes was realized. The experimental results demonstrate the method has preferable classification effect.