A review of some Bayesian Belief Network structure learning algorithms

ICICS 2011 - 8th International Conference on Information, Communications and Signal Processing

Volumn , Issue , 2011, Pages

  Mittal, Sangeeta ^a   Maskara, S L ^b  

^a JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b Soura Niloy Complex   (India)

Author keywords

Bayesian Belief Network; Hepatitis Domain Diseases; Structure Learning; Urinary System Diseases

Indexed keywords

DATA SETS; GRAPHICAL MODEL; NETWORK STRUCTURES; STRUCTURE-LEARNING; URINARY SYSTEM;

DECISION THEORY; LEARNING ALGORITHMS; SIGNAL PROCESSING;

BAYESIAN NETWORKS;

EID: 84860629033     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICICS.2011.6173579     Document Type: Conference Paper

References (14)

1. S. B. Kotsiantis, "Supervised Machine Learning: A Review of Classification Techniques", In Proceeding of conference on Emerging Artificial Intelligence Applications in Computer Engineering, 2007.
2. D. Heckerman, A. Mamdani, and M. P. Wellman, "Real-world applications of Bayesian networks", Commun. ACM 38, 3 (March 1995), 24-2. 1995.
3. E Philippot , Y Belaid and A. Belaid, "Bayesian Networks Learning Algorithms for Online Form Classification", In Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10) (2010).
4. Spirtes P, Glymour C, Scheines R : Causation, prediction, and search, 2nd edn. MIT press, Cambridge (2000)
5. J Cheng et al, "Learning Bayesian networks from data: an information theory based approach", Artif Intell 137:43-90 , 2002
6. D Heckerman, D Geiger, D M Chickering, "Learning Bayesian networks: the combination of knowledge and statistical data", Mach Learn 20(3):197-243(1995)
7. T. Roos, T. Silander, P. Kontkanen, and P. Myllym "Bayesian network structure learning using factorized NML universal models", In Proc. ITA'08, 2008.
8. C. Chow and C. Liu, "Approximating discrete probability distributions with dependence trees", IEEE Transactions on Information Theory, 14(3):462-467, 1968.
9. D. Chickering, D. Geiger, and D. Heckerman, "Learning bayesian networks: Search methods and experimental results", In Proceedings of Fifth Conf. on Artificial Intelligence and Statistics, pages 112-128, 1995.
10. GF Cooper, E Herskovits, "A Bayesian method for the induction of probabilistic networks from data", Mach Learn 9(4):309-347(1992)
11. P Leray , Francois O : BNT structure learning package: documentation and experiments. Technical Report 2004/PhLOF, PSI, LITIS Laboratory.
12. UCI repository of machine learning databases. Available at: http://archive. ics. uci.edu/ml/
13. B. Wemmenhove et al., "Inference in the promedas medical expert system", In Artificial intelligence in medicine, volume 4594/2007 of lecture notes in computer science. Springer, Berlin, pp 456-460 (2007)
14. D. Margaritis, " Learning Bayesian network model structure from data", Technical Report CMU-CS-03-153 (2003)