基于混沌微粒群算法的文本分类研究*

针对中文文本分类问题，将其用于分类规则的抽取。为了避免微粒群算法在全局优化中陷入局部极值，利用混沌运动遍历性、随机性等特点，对标准微粒群算法进行了改进，提出了基于混沌微粒群算法的文本自动分类方法。仿真实验表明本算法对文档进行分类是一种比较可行的分类方法，分类精度高、速度快。

Research of Chinese text categorization based on chaotic particle swarm optimization

As to the problem of Chinese text classification, it was used to extract classification rules. To solve the premature problem of PSO, utilized the ergodicity and randomicity of chaos to improve the conventional PSO algorithm, then proposed a new text classification method based on chaos-PSO. Experiment results show that this method is feasible for Chinese text categorization, it has good precision and high time efficiency.