A novel naive bayes classification algorithm based on particle swarm optimization

被引：0

作者：

Li, Jun ^{[1
,2
,3
]}

Ding, Lixin ^{[1
,2
]}

Li, Bo ^{[3
]}

机构：

[1] State Key Laboratory of Software Engineering, Wuhan University, Wuhan

[2] School of Computer, Wuhan University, Wuhan

[3] College of Computer Science and Technology, Wuhan University of Science and Technology, WUST, Wuhan

来源：

Open Automation and Control Systems Journal | 2014年 / 6卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Attribute subset; Classification accuracy; Feature selection; Naive Bayes; Particle swarm optimization algorithm;

D O I：

10.2174/1874444301406010747

中图分类号：

学科分类号：

摘要：

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called particle swarm optimization-naive Bayes (PSO-NB) which takes advantage of combination particle swarm optimization with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies PSO firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Decision Tree(C4.5) algorithm, K-neighbor(KNN) algorithm, the proposed algorithm has higher classification accuracy. © Li et al.; Licensee Bentham Open.

引用

页码：747 / 753

页数：6

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