Robust inference of Bayesian networks using speciated evolution and ensemble

Kyung Joong Kim, Ji Oh Yoo, Sung Bae Cho

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

5 Citations (Scopus)

Abstract

Recently, there are many researchers to design Bayesian network structures using evolutionary algorithms but most of them use the only one fittest solution in the last generation. Because it is difficult to integrate the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In the experiments with Asia network, the proposed method provides with better robustness for handling uncertainty owing to the complicated redundancy with speciated evolution.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages92-101
Number of pages10
Publication statusPublished - 2005 Dec 1
Event15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005 - Saratoga Springs, NY, United States
Duration: 2005 May 252005 May 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3488 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005
CountryUnited States
CitySaratoga Springs, NY
Period05/5/2505/5/28

Fingerprint

Robust Inference
Bayesian networks
Bayesian Networks
Network Structure
Ensemble
Bayes Theorem
Function evaluation
Bayesian Methods
Evaluation Function
Evolutionary algorithms
Fitness
Uncertainty
Redundancy
Biased
Evolutionary Algorithms
Sharing
Integrate
Research Personnel
Robustness
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, K. J., Yoo, J. O., & Cho, S. B. (2005). Robust inference of Bayesian networks using speciated evolution and ensemble. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 92-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3488 LNAI).
Kim, Kyung Joong ; Yoo, Ji Oh ; Cho, Sung Bae. / Robust inference of Bayesian networks using speciated evolution and ensemble. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. pp. 92-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Kim, KJ, Yoo, JO & Cho, SB 2005, Robust inference of Bayesian networks using speciated evolution and ensemble. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3488 LNAI, pp. 92-101, 15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005, Saratoga Springs, NY, United States, 05/5/25.

Robust inference of Bayesian networks using speciated evolution and ensemble. / Kim, Kyung Joong; Yoo, Ji Oh; Cho, Sung Bae.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 92-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3488 LNAI).

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

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Kim KJ, Yoo JO, Cho SB. Robust inference of Bayesian networks using speciated evolution and ensemble. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 92-101. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).