Learning Bayesian networks in the space of structures by estimation of distribution algorithms

被引：44

作者：

Blanco, R ^{[1
]}

Inza, I ^{[1
]}

Larrañga, P ^{[1
]}

机构：

[1] Univ Basque Country, Dept Comp Sci & Artificial Intelligence, Intelligent Syst Grp, E-20080 San Sebastian, Spain

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2003年 / 18卷 / 02期

关键词：

D O I：

10.1002/int.10084

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The induction of the optimal Bayesian network structure is NP-hard, justifying the use of search heuristics. Two novel population-based stochastic search approaches, univariate marginal distribution algorithm (UMDA) and population-based incremental learning (PBIL), are used to learn a Bayesian network structure from a database of cases in a score + search framework. A comparison with a genetic algorithm (GA) approach is performed using three different scores: penalized maximum likelihood, marginal likelihood, and information-theory-based entropy. Experimental results show the interesting capabilities of both novel approaches with respect to the score value and the number of generations needed to converge. (C) 2003 Wiley Periodicals, Inc.

引用

页码：205 / 220

页数：16

共 17 条

[1] A guide to the literature on learning probabilistic networks from data [J].