Improve the Accuracy of One Dependence Augmented Naive Bayes by Weighted Attribute

被引：0

作者：

Jiang, Siwei ^{[1
]}

Cai, Zhihua ^{[1
]}

机构：

[1] China Univ Geosci, Fac Comp Sci, Wuhan 430074, Peoples R China

来源：

ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS | 2008年 / 5370卷

关键词：

Naive Bayes; selective attribute; weighted attribute; support vector machine; classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Naive Bayes is a effective and widely used data ruining algorithm for classification, but its Unrealistic attribute conditional independence harm its performance. Selecting attributes subsets is an important approach to extend the Naive Bayes, and the state-of-the-art SBC algorithm has better accuracy in classification. In this paper, we review the weighted attribute method for Naive Bayes, and explain SBC is one of the special case in weighted attributed Methods. Interesting this method, we present a new one dependence augmented Naive Bayes with weighted attribute called WODANB, which use the fuzzy Support Vector Machine to optimize the weights. Experiment on whole 36 datasets recommended by Weka, results show that, WODANB significant outperforms than NB, SBC, ODANB, TAN.

引用

页码：556 / 561

页数：6