基于粒子群优化的朴素贝叶斯改进算法  

Improved Native Bayes Algorithm Based on Particle Swarm Optimization

作　　者：邱宁佳[1] 李娜 胡小娟[1] 王鹏[1] 孙爽滋[1] QIU Ningjia;LI Na;HU Xiaojuan;WANG Peng;SUN Shuangzi(College of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China)

机构地区：[1]长春理工大学计算机科学技术学院,长春130022

出　　处：《计算机工程》2018年第11期27-32,39,共7页Computer Engineering

基　　金：吉林省科技发展计划重点科技攻关项目(20150204036GX);吉林省省级产业创新专项资金(2017C051)

摘　　要：针对朴素贝叶斯(NB)算法因条件独立性的理想式假设引起分类性能降低的问题,提出一种改进的粒子群优化-朴素贝叶斯(PSO-NB)算法。在文本预处理时,引入权重因子、类内和类间离散因子进行属性约简,基于NB加权模型,将条件属性的词频比率作为其初始权值,利用PSO算法迭代寻找全局最优特征权向量,并以此权向量作为加权模型中各个特征词的权值生成分类器。运用经典数据集对PSO-NB算法进行性能分析,结果表明,改进算法可有效减少冗余属性,降低计算复杂度,具有较高的准确率和召回率。Aiming at the problem of classification performance degradation caused by the idealized assumption of conditional independence of Naive Bayes(NB)algorithm,an improved Particle Swarm Optimization-Native Bayes(PSO-NB)algorithm is proposed.In text preprocessing,weight factor,intra-class and inter-class discrete factors are introduced for attribute reduction.Based on NB weighted model,the word-frequency ratio of conditional attribute is used as its initial weight,and PSO algorithm is used to iteratively find global optimal feature weight vector.The vector is used as a weight value to generate a classifier for each feature word in the weighting model.The performance analysis of PSO-NB algorithm is done using classical dataset.Result shows that the improved algorithm can effectively reduce redundant attributes,reduce computational complexity,and has high accuracy and recall rate.

关 键 词：朴素贝叶斯 互信息 属性约简 粒子群优化算法 权值优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]