A new genetic approach to structure learning of Bayesian networks

Jaehun Lee, Wooyong Chung, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, a new approach to structure learning of Bayesian networks (BNs) based on genetic algorithm is proposed. The proposed method explores the wider solution space than the previous method. In the previous method, while the ordering among the nodes of the BNs was fixed their conditional dependencies represented by the connectivity matrix was learned, whereas, in the proposed method, the ordering as well as the conditional dependency among the BN nodes is learned. To implement this method using the genetic algorithm, we represent an individual of the population as a pair of chromosomes: The first one represents the ordering among the BN nodes and the second one represents their conditional dependencies. To implement proposed method new crossover and mutation operations which are closed in the set of the admissible individuals are introduced. Finally, a computer simulation exploits the real-world data and demonstrates the performance of the method.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings
PublisherSpringer Verlag
Pages659-668
Number of pages10
ISBN (Print)354034439X, 9783540344391
Publication statusPublished - 2006 Jan 1
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 2006 May 282006 Jun 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3971 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
CountryChina
CityChengdu
Period06/5/2806/6/1

Fingerprint

Structure Learning
Bayesian networks
Bayesian Networks
Genetic algorithms
Chromosomes
Vertex of a graph
Genetic Algorithm
Computer simulation
Chromosome
Crossover
Connectivity
Mutation
Computer Simulation
Closed
Demonstrate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lee, J., Chung, W., & Kim, E. (2006). A new genetic approach to structure learning of Bayesian networks. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings (pp. 659-668). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS). Springer Verlag.
Lee, Jaehun ; Chung, Wooyong ; Kim, Euntai. / A new genetic approach to structure learning of Bayesian networks. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Springer Verlag, 2006. pp. 659-668 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Lee, J, Chung, W & Kim, E 2006, A new genetic approach to structure learning of Bayesian networks. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3971 LNCS, Springer Verlag, pp. 659-668, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 06/5/28.

A new genetic approach to structure learning of Bayesian networks. / Lee, Jaehun; Chung, Wooyong; Kim, Euntai.

Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Springer Verlag, 2006. p. 659-668 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper, a new approach to structure learning of Bayesian networks (BNs) based on genetic algorithm is proposed. The proposed method explores the wider solution space than the previous method. In the previous method, while the ordering among the nodes of the BNs was fixed their conditional dependencies represented by the connectivity matrix was learned, whereas, in the proposed method, the ordering as well as the conditional dependency among the BN nodes is learned. To implement this method using the genetic algorithm, we represent an individual of the population as a pair of chromosomes: The first one represents the ordering among the BN nodes and the second one represents their conditional dependencies. To implement proposed method new crossover and mutation operations which are closed in the set of the admissible individuals are introduced. Finally, a computer simulation exploits the real-world data and demonstrates the performance of the method.

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Lee J, Chung W, Kim E. A new genetic approach to structure learning of Bayesian networks. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Springer Verlag. 2006. p. 659-668. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).