基于混沌二进制粒子群优化的KNN文本分类算法  

KNN Text Classification Algorithm Based on Chaotic Binary Particle Swarm Optimization

作　　者：徐辉[1] 

机构地区：[1]广西财经学院信息与统计学院,广西南宁530003

出　　处：《微电子学与计算机》2012年第8期204-208,共5页Microelectronics & Computer

基　　金：广西科技攻关与新产品试制项目(桂科攻10123012-7)

摘　　要：中文文本分类的主要问题是特征空间的高维性.提出了基于混沌二进制粒子群的KNN文本分类算法,利用混沌二进制粒子群算法遍历训练集的特征空间,选择特征子空间,然后在特征子空间中使用KNN算法进行文本分类.在粒子群的迭代优化过程中,利用混沌映射,指导群体进行混沌搜索,使算法摆脱局部最优,扩大寻找全局最优解的能力.实验结果表明,提出的新分类算法对中文文本分类是有效的,其分类准确率、召回率都优于KNN算法.The main problem of Chinese text classification is the high dimenmonat teature space particle swarm optimization, KNN text classification algorithm is proposed. It uses chaotic particle swarm algorithm to traverse feature space of the training set, selects the feature subspace, and then it uses KNN algorithm to classify text in feature subspace. In particle swarm＇ s iterative process, It uses chaotic map to guide swarms for chaotic search,it makes the algorithm out of local optimum, and expands the ability of finding global optimal solution. Experimental results show that the proposed new classification algorithm for Chinese text classification is effective, the classification accuracy and recall are better than KNN algorithm.

关 键 词：二进制粒子群 混沌 K最近邻 文本分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]