Learning Bayesian network equivalence classes from incomplete data

被引：0

作者：

Borchani, Hanen ^{[1
]}

Ben Amor, Nahla ^{[1
]}

Mellouli, Khaled ^{[1
]}

机构：

[1] LARODEC, Inst Super Gest Tunis, Le Bardo 2000, Tunisia

来源：

DISCOVERY SCIENCE, PROCEEDINGS | 2006年 / 4265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：

This paper proposes a new method, named Greedy Equivalence Search-Expectation Maximization (GES-EM), for learning Bayesian networks from incomplete data. Our method extends the recently proposed GES algorithm to deal with incomplete data. Evaluation of generated networks was done using expected Bayesian Information Criterion (BIC) scoring function. Experimental results show that GES-EM algorithm yields more accurate structures than the standard Alternating Model Selection-Expectation Maximization (AMS-EM) algorithm.

引用

页码：291 / 295

页数：5

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