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

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.