Approximate Bayesian network classifiers

被引：0

作者：

Slezak, D ^{[1
]}

Wróblewski, J ^{[1
]}

机构：

[1] Polish Japanese Inst Informat Technol, PL-02008 Warsaw, Poland

来源：

ROUGH SETS AND CURRENT TRENDS IN COMPUTING, PROCEEDINGS | 2002年 / 2475卷

关键词：

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：

Bayesian network (BN) is a directed acyclic graph encoding probabilistic independence statements between variables. BN with decision attribute as a root can be applied to classification of new cases, by synthesis of conditional probabilities propagated along the edges. We consider approximate BNs, which almost keep entropy of a decision table. They have usually less edges than classical BNs. They enable to model and extend the well-known Naive Bayes approach. Experiments show that classifiers based on approximate BNs can be very efficient.

引用

页码：365 / 372

页数：8

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