Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm

Man Leung WONG, Yuan Yuan GUO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.
Original languageEnglish
Title of host publicationProceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006
PublisherIEEE Computer Society
Pages1146-1150
Number of pages5
DOIs
Publication statusPublished - 1 Jan 2006

Fingerprint

Bayesian networks
Evolutionary algorithms
Learning algorithms
Marketing

Bibliographical note

Paper presented at the 6th International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong.
ISBN of the source publication: 9780769527017

Cite this

WONG, M. L., & GUO, Y. Y. (2006). Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm. In Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006 (pp. 1146-1150). IEEE Computer Society. https://doi.org/10.1109/ICDM.2006.56
WONG, Man Leung ; GUO, Yuan Yuan. / Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm. Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006. IEEE Computer Society, 2006. pp. 1146-1150
@inproceedings{043b9619584b433c876671ab8c7f649f,
title = "Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm",
abstract = "This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.",
author = "WONG, {Man Leung} and GUO, {Yuan Yuan}",
note = "Paper presented at the 6th International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong. ISBN of the source publication: 9780769527017",
year = "2006",
month = "1",
day = "1",
doi = "10.1109/ICDM.2006.56",
language = "English",
pages = "1146--1150",
booktitle = "Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006",
publisher = "IEEE Computer Society",
address = "United States",

}

WONG, ML & GUO, YY 2006, Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm. in Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006. IEEE Computer Society, pp. 1146-1150. https://doi.org/10.1109/ICDM.2006.56

Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm. / WONG, Man Leung; GUO, Yuan Yuan.

Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006. IEEE Computer Society, 2006. p. 1146-1150.

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

TY - GEN

T1 - Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm

AU - WONG, Man Leung

AU - GUO, Yuan Yuan

N1 - Paper presented at the 6th International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong. ISBN of the source publication: 9780769527017

PY - 2006/1/1

Y1 - 2006/1/1

N2 - This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.

AB - This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.

UR - http://commons.ln.edu.hk/sw_master/7067

U2 - 10.1109/ICDM.2006.56

DO - 10.1109/ICDM.2006.56

M3 - Conference paper (refereed)

SP - 1146

EP - 1150

BT - Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006

PB - IEEE Computer Society

ER -

WONG ML, GUO YY. Discover Bayesian networks from incomplete data using a hybrid evolutionary algorithm. In Proceedings of the 6th IEEE International Conference on Data Mining, ICDM 2006. IEEE Computer Society. 2006. p. 1146-1150 https://doi.org/10.1109/ICDM.2006.56