Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms

Authors:
Fengzhan Tian;Yuchang Lu;Chunyi Shi
Affiliations:
-;-;-
Venue:
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Year:
2001

Citing 7
Cited 3

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
The EM algorithm for graphical association models with missing data

Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Probabilistic Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Learning Belief Networks in the Presence of Missing Values and Hidden Variables

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Uniform Crossover in Genetic Algorithms

Proceedings of the 3rd International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature

Learning Bayesian Networks from Incomplete Data Based on EMI Method

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning Bayesian Networks Based on a Mutual Information Scoring Function and EMI Method

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
An improved bayesian network learning algorithm based on dependency analysis

CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new method, called EM-EA, is put forward for learning Bayesian network structures from incomplete data. This method combines the EM algorithm with an evolutionary algorithm (EA) and transforms the incomplete data to complete data using EM algorithm and then evolve network structures using the evolutionary algorithm with the complete data. In order to learn Bayesian networks with hidden variables, a new mutation operator has been introduced and the function of the crossover has been correspondingly expanded. The results of the experiments show that EM-EA is more accurate and practical than other network structure learning algorithms that deal with the incomplete data.