An evolutionary algorithmic approach to learning a Bayesian network from complete data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5433, Issue , 2004, Pages 88-99

  Sahin, Ferat ^a   Tillett, Jason ^a   Rao, Raghuveer ^a   Rao, T M ^b  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

^b STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Bayesian networks; Data mining; Knowledge representation; Large databases; Particle swarm optimization; Structural learning

Indexed keywords

BAYESIAN NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); STRUCTURAL LEARNING;

DATA MINING; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; HEURISTIC METHODS; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION;

DATABASE SYSTEMS;

EID: 3543146577     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.542371     Document Type: Conference Paper

References (28)

1. D. J. Spiegelhalter and R. G. Cowell, "Learning in probabilistic expert systems," Bayesian Statistics, vol. 4, 1992.
2. E. Herskovits, "Computer-based probabilistic network construction," in Medical Informatics: Stanford University, 1991.
3. D. M. Chickering, D. Gieger, and et.al., "Learning Bayesian network is NP-Hard," Micrsoft Research, Redmaond, WA 1994.
4. D. Koller, "Artificial intelligence: Knowledge representation and reasoning under uncertainty, Course material (CS 288). " http://www.stanford.edu/class/cs228/index.html. 1999.
5. G. F. Cooper and E. Herskovits, "A Bayesian method for constructing Bayesian belief networks from databases," Proceedings of the Conference on Uncertainty in AI, pp. 86-94, 1990.
6. F. Sahin and J. S. Bay, "Structural Bayesian network learning in a biological decision-theoretic intelligent agent and its application to a herding problem in the context of distributed multi-agent systems," Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2001), pp. 1606-1611, 2001.
7. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by Simulated Annealing," Science, vol. 264(5163), pp. 1297-1301, 1983.
8. J. Holland, Adaptation in Natural and Artificial Systems: University of Michigan Press, 1975.
9. J. Kennedy and R. Eberhart, Swarm Intellignce: Morgan Kaufmann Publishers, ISBN 1-55860-595-9, 2001.
10. A. Salman, I. Ahmad, and S. Al-Madani, "Particle swarm optimization for task assignment problem," Microprocessors and Microsystems, vol. 26, pp. 363-371, 2002.
11. F. V. Jensen, An Introduction to Bayesian Networks. London, UK: University College London Press, 1996.
12. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann, 1988.
13. K. G. Olesen, S. L. Lauritzen, and F. V. Jensen, "aHUGIN: A system for creating adaptive causal probabilistic networks," Proceedings of the Eighth Conference on Uncertainty in AI (UAI '92), Stanford, CA, 1992.
14. D. Spiegelhalter, P. Dawid, S. L. Lauritzen, and R. Cowell, "Bayesian analysis in expert systems," Statistical Science, vol. 8, pp. 219-282, 1993.
15. G. Cooper and E. Herskovits, "A Bayesian method for induction of probabilistic networks from data," Machine Learning, vol. 9, pp. 309-347, 1992.
16. D. Heckerman, D. Gieger, and M. Chickering, "Learning Bayesian networks: The combination of knowledge and statistical data," Technical Report MSR-TR-94-09, Microsoft Research, Redmond, WA 1994.
17. S. Russell, J. Binder, and D. Koller, "Adaptive probabilistic networks," Technical Report UCB//CSD-94-824, 1994.
18. R. W. Robinson, "Counting Unlabeled Acyclic Digraphs," in Lecture Notes in Mathematics,622:Combinatorial Mathematics V., C. H. C. Little(Ed.), Ed. New York: Springer-Verlag, 1977.
19. W. Lam and F. Bacchus, "Learning Bayesian belief networks: an approach based on the MDL principle," Computational Intelligence, vol. 10, pp. 269-293, 1994.
20. G. Schwarz, "Estimation the dimension of a model," Annals of Statistics, vol. 6, pp. 462-464, 1978.
21. D. Heckerman, "A tutorial on learning Bayesian networks," Technical Report MSR-TR-95-06, Microsoft Research 1995.
22. F. Glover, "Tabu search 1," ORSA Journal on Computing, vol. 1(3), pp. 190- 206, 1989.
23. P. P. Larranaga, M.; Yurramendi, Y.; Murga, R.H.; Kuijpers, C.M.H., "Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 912-926, 1996.
24. J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, pp. 4104-4109, 1997.
25. J. C. Tillett and F. Sahin, "IntelliAgent: Simulation Environment for Cooperating Intelligent Agents," Proceedings of the IEEE SMC2002 Conference, vol. 3, 2002.
26. F. Sahin, "A Bayesian approach to the self-organization and learning in intelligent agents," Dissertation: Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg. 2000.
27. K. Murphy, "Bayes Net Toolbox for Matlab", http://www.ai.mit. edu/~murphyk/Software/BNT/bnt.htrnl, 2004.
28. I. A. Beinlich, H. J. Suermondt, R. M. Chavez, and G. F. Cooper, "The ALARM Monitoring System: A case study with two probabilistic techniques for belief networks," Proceedings of the Second European Conference on Artificial Intelligence in Medicine, vol. 38, pp. 247-256, 1989.