基于支持向量机的垃圾信息过滤方法

针对垃圾信息过滤的问题,提出了一种基于支持向量机(SVM)算法的垃圾信息过滤方法. 利用文本分类和信息检索领域所常用的性能评价指标,建立了垃圾信息过滤的评价体系,针对仿真实验获得的实验数据,利用所建立的垃圾信息过滤评价体系对实验数据评价结果,选取了适合的核函数及其参数,构建了SVM分类器,同时也通过仿真实验和评价体系对SVM分类器和传统贝叶斯分类器进行了测试和评估. 结果表明,基于SVM算法的分类器提高了信息过滤的准确性,同时也验证了SVM算法在垃圾信息过滤中的有效性. 

Filtering Methods Against Junk Messages Based on Support Vector Machine

A junk messages filtering method, based on an algorithm of support vector machine (SVM), is proposed. By means of performance evaluation index universally applied in such fields as text classification and information retrieval, an evaluation system for filtering junk messages is established. On the basis of data obtained in stimulation and by means of evaluation results of the evaluation system for filtering junk messages on experimental data, the proper kernel function and its parameters are selected to construct a classifier of support vector machine; The classifier of support vector machine and the traditional Bayesian classifier are tested and evaluated through stimulation and evaluation system. The comparison results show that the classifier of support vector machine improve the accuracy of filtering messages, and verify the effectiveness of the algorithm of support vector machine in junk messages filtering.