Name
Title | ||
|---|---|---|
A New Bayesian Network Structure Learning Algorithm Mechanism Based On The Decomposability Of Scoring Functions |
Abstract | ||
|---|---|---|
Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MIMBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1587/transfun.E100.A.1541 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
Bayesian network, structure learning, mutual information, the decomposability of score functions, binary particle swarm optimization | Variable-order Bayesian network,Structure learning,Binary particle swarm optimization,Bayesian network,Artificial intelligence,Mutual information,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
E100A | 7 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoliang Li | 1 | 16 | 4.97 |
| Lining Xing | 2 | 16 | 8.51 |
| Z. Zhang | 3 | 146 | 18.47 |
| Ying-Wu Chen | 4 | 205 | 19.89 |