Efficient learning bayesian networks using PSO

Authors:
Tao Du;S. S. Zhang;Zongjiang Wang
Affiliations:
Shanghai Jiaotong University, Shanghai, China;Shanghai Jiaotong University, Shanghai, China;Shanghai Jiaotong University, Shanghai, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 4
Cited 0

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on learning with Bayesian networks

Learning in graphical models
An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we firstly introduce particle swarm optimization to the problem of learning Bayesian networks and propose a novel structure learning algorithm using PSO. To search in DAG spaces efficiently, a discrete PSO algorithm especially for structure learning is proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and could obtain better structures compared with GA based algorithms.