The week's pick
Random Articles
Reseach Article
CCGA-BN Constructor: A Bayesian Network Learning Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 115 - Number 20 |
| Year of Publication: 2015 |
| Authors: Maryam Feroze, Muhammad Saeed, Nasir Touheed |
10.5120/20266-2672
|
Maryam Feroze, Muhammad Saeed, Nasir Touheed . CCGA-BN Constructor: A Bayesian Network Learning Approach. International Journal of Computer Applications. 115, 20 ( April 2015), 9-15. DOI=10.5120/20266-2672
Abstract
This paper presents a tool CCGA-BN Constructor for learning Bayesian network that uses cooperative co-evolutionary genetic algorithm to learn Bayesian network structure from data. The problem has been broken down into two sub-problems: (a) to find the optimal nodes'ordering and (b) to find the optimal adjacency matrix of the graph. Both the sub-problems' solutions are then combined to produce the optimal structure. CCGA-BN constructor used Bayesian score for networks having nodes with more than two states and BIC for network having bistate nodes. The findings of this paper are compared against the original structures and the results show a lot of promise.
References
- D. Heckerman, D. Geiger, and D. M. Chickering. "Learning Bayesian networks: The combination ofknowledge and statistical data". Machine Learning, 20:197–243, 1995.
- G. F. Cooper and E. Herskovits. "A Bayesian method for the induction of probabilistic networks from data". Machine Learning, 9:309–347, 1992.
- D. M. Chickering, D. Geiger, and D. Heckerman. "Learning Bayesian Networks is NP-Hard". Technical report, Microsoft Research, 1994.
- C. Cotta and J. Muruzabal. "On the learning of Bayesian network graph structures via evolutionary programming". In Proceedings of the 2nd European Workshop on Probabilistic Graphical Models, pages 65–72, 2004.
- P. Larranaga and M. Poza. "Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters". IEEE Journal on Pattern Analysis and Machine Intelligence, 18(9):912–926, 1996
- M. Wong, W. Lam, and K. Leung. "Using evolutionary programming and minimum description length principle for data mining of Bayesian networks". IEEE Transactions PAMI, 21(2):174–178, 1999.
- M. L. Wong, S. Y. Lee, and K. -S. Leung. "A hybrid data mining approach to discover bayesian networks using evolutionary programming". In GECCO, pages 214–222, 2002.
- Arthur Carvalho, David Cheriton. "A Cooperative Coevolutionary Genetic Algorithm for Learning Bayesian Network Structures". School of Computer Science, University of Waterloo, Ontario, Canada. July 2011.
- D. Koller and N. Friedman. "Probabilistic Graphical Models: Principles and Techniques". MIT Press, 2009.
- Spirtes, Glymour, AndScheines. "Causation, Prediction And Search". Springer-Verlag, 1993.
- R. W. Robinson. "Counting Unlabeled Acyclic Digraphs". In Combinatorial Mathematics V, volume 622 of Lecture Notes in Mathematics, pages 28–43,1977.
- M. Potter and K. De Jong. "A cooperative coevolutionary approach to function optimization". In Third Conference on Parallel Problem Solving from Nature, pages 249–257, 1994.
- A. Delaplace, T. Brouard, and H. Cardot. "Two Evolutionary Methods for Learning Bayesian Network Structure"s. In Computational Intelligence and Security, pages 288–297. 2007.
- Mitchell A. Potter and Kenneth A. De Jong "A Cooperative Coevolutionary Approach to Function Optimization". Computer Science Department, George Mason University, Fairfax, VA 22030, USA.
- J. Pearl. "Probabilistic reasoning in intelligent systems: networks of plausible inference". Morgan Kaufmann, 1997.
- Alexandra M. Carvalho "Scoring functions for learning Bayesian networks". Inesc-id Tec. Rep, 2009
Index Terms
Keywords