An artificial bee colony algorithm for learning Bayesian networks

被引：47

作者：

Ji, Junzhong ^{[1
]}

Wei, Hongkai ^{[1
]}

Liu, Chunnian ^{[1
]}

机构：

[1] Beijing Univ Technol, Coll Comp Sci & Technol, Beijing Municipal Key Lab Multimedia & Intelligen, Beijing, Peoples R China

来源：

SOFT COMPUTING | 2013年 / 17卷 / 06期

基金：

北京市自然科学基金;

关键词：

Bayesian networks; Structure learning; Stochastic search; Artificial bee colony algorithm; DESCRIPTION LENGTH PRINCIPLE; PROBABILISTIC NETWORKS; PERFORMANCE;

D O I：

10.1007/s00500-012-0966-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic Algorithm, Evolutionary Programming and Ant Colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.

引用

页码：983 / 994

页数：12

共 26 条

[1]

[Anonymous], 2005, Technical Report-TR06

[2]

[Anonymous], 2010, 2010 43 HAW INT C SY

[3]

Beinlich I., 1989, PROC 2 EUROPEAN C ME, P247

[4] Adaptive probabilistic networks with hidden variables [J].

Binder, J ;

Koller, D ;

Russell, S ;

Kanazawa, K .

MACHINE LEARNING, 1997, 29 (2-3) :213-244

[5] Learning Bayesian networks from data: An information-theory based approach [J].

Cheng, J ;

Greiner, R ;

Kelly, J ;

Bell, D ;

Liu, WR .

ARTIFICIAL INTELLIGENCE, 2002, 137 (1-2) :43-90

[6] A BAYESIAN METHOD FOR THE INDUCTION OF PROBABILISTIC NETWORKS FROM DATA [J].

COOPER, GF ;

HERSKOVITS, E .

MACHINE LEARNING, 1992, 9 (04) :309-347

[7] Ant colony optimization for learning Bayesian networks [J].

de Campos, LM ;

Fernández-Luna, JM ;

Gámez, JA ;

Puerta, JM .

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2002, 31 (03) :291-311

[8] A new approach for learning belief networks using independence criteria [J].

de Campos, LM ;

Huete, JF .

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2000, 24 (01) :11-37

[9]

Friedman N, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P206

[10] Searching for the best elimination sequence in Bayesian networks by using ant colony optimization [J].

Gámez, JA ;

Puerta, JM .

PATTERN RECOGNITION LETTERS, 2002, 23 (1-3) :261-277

← 1 2 3 →