Evolutionary aggregation and refinement of Bayesian networks

Kyung Joong Kim, Simg Bae Cho

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

2 Citations (Scopus)

Abstract

Bayesian network (BN) is a useful tool to represent joint probability distribution in the form of graphical model providing flexible inference and uncertainty handling. If there is enough knowledge about domain, it is possible to design the structure and parameters of BN by expert. Also, it can be learned from massive dataset with statistical learning algorithm. Usually, because the search space of Bayesian networks is relatively huge compared to the other models, evolutionary algorithms have been used to find optimal structure and parameters by many researchers. In this paper, we have focused on the topic of adaptation of constructed models for better performance. If there are a number of models constructed or learned by different experts or sources, it is better to fuse them into one model by considering all the information of each model. However, the complexity of the integrated model is relatively higher than previous isolated models. Minimizing the complexity of the integrated model using evolutionary algorithm is proposed. After integrating models into single one, it needs to adapt to the new data from the environment. It is likely to provide wrong results to the newly generated data from the environment and slightly modifying the joint probability distribution is necessary. The refinement process is also guided by the evolutionary algorithm because the space of search is large. Experimental results on a benchmark network show that the proposed adaptation methods with evolutionary algorithm can perform better than heuristics or greedy approaches.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages1513-1520
Number of pages8
Publication statusPublished - 2006 Dec 1
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: 2006 Jul 162006 Jul 21

Publication series

Name2006 IEEE Congress on Evolutionary Computation, CEC 2006

Other

Other2006 IEEE Congress on Evolutionary Computation, CEC 2006
CountryCanada
CityVancouver, BC
Period06/7/1606/7/21

Fingerprint

Bayesian networks
Bayesian Networks
Aggregation
Refinement
Agglomeration
Evolutionary Algorithms
Evolutionary algorithms
Integrated Model
Joint Distribution
Model
Probability Distribution
Probability distributions
Statistical Learning
Graphical Models
Search Space
Learning Algorithm
Likely
Electric fuses
Heuristics
Benchmark

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Kim, K. J., & Cho, S. B. (2006). Evolutionary aggregation and refinement of Bayesian networks. In 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 1513-1520). [1688488] (2006 IEEE Congress on Evolutionary Computation, CEC 2006).
Kim, Kyung Joong ; Cho, Simg Bae. / Evolutionary aggregation and refinement of Bayesian networks. 2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. pp. 1513-1520 (2006 IEEE Congress on Evolutionary Computation, CEC 2006).
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Kim, KJ & Cho, SB 2006, Evolutionary aggregation and refinement of Bayesian networks. in 2006 IEEE Congress on Evolutionary Computation, CEC 2006., 1688488, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1513-1520, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, Vancouver, BC, Canada, 06/7/16.

Evolutionary aggregation and refinement of Bayesian networks. / Kim, Kyung Joong; Cho, Simg Bae.

2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 1513-1520 1688488 (2006 IEEE Congress on Evolutionary Computation, CEC 2006).

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

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Kim KJ, Cho SB. Evolutionary aggregation and refinement of Bayesian networks. In 2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 1513-1520. 1688488. (2006 IEEE Congress on Evolutionary Computation, CEC 2006).