Classification using markov blanket for feature selection

2009 IEEE International Conference on Granular Computing, GRC 2009

Volumn , Issue , 2009, Pages 743-747

  Zeng, Yifeng ^a   Luo, Jian ^b   Lin, Shuyuan ^c  

^a AALBORG UNIVERSITY   (Denmark)

^b XIAMEN UNIVERSITY   (China)

^c FUZHOU UNIVERSITY   (China)

Author keywords

Classification; Feature selection; Markov blanket

Indexed keywords

CLASSIFICATION FEATURES; CLASSIFICATION PERFORMANCE; CLASSIFICATION TASKS; FEATURE SELECTION; FEATURE SELECTION METHODS; INDUCTION ALGORITHMS; LARGE DATA; MARKOV BLANKETS; MINIMAL SUBSET; OPTIMAL CLASSIFICATION;

CLASSIFIERS; COMPUTER SCIENCE; GRANULAR COMPUTING;

LEARNING SYSTEMS;

EID: 70449858526     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GRC.2009.5255023     Document Type: Conference Paper

References (28)

1. Langley, P.: Selection of relevant features in machine learning. In: In Proceedings of the AAAI Fall symposium on relevance, AAAI Press (1994) 140-144
2. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1 (1997) 131-156
3. Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17 (2005) 491-502
4. Almuallim, H., Dietterich, T. G.: Learning with many irrelevant features. In: In Proceedings of the Ninth National Conference on Artificial Intelligence, AAAI Press (1991) 547-552
5. Kohavi, R., John, G. H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2) (1997) 273-324
6. Koller, D., Sahami, M.: Toward optimal feature selection. In: Proceedings of the Thirteenth International Conference on Machine Learning, Morgan Kaufmann (1996) 284-292
7. Yaramakala, S., Margaritis, D.: Speculative markov blanket discovery for optimal feature selection. In: ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining, Washington, DC, USA, IEEE Computer Society (2005) 809-812
8. Tsamardinos, I., Aliferis, C. F.: Towards principled feature selection: Relevancy, filters and wrappers. In: in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Morgan Kaufmann Publishers (2003)
9. Margaritis, D., Thrun, S.: Bayesian network induction via local neighborhoods. In: in Advances in Neural Information Processing Systems 12 (NIPS, MIT Press (1999) 505-511
10. Tsamardinos, I., Aliferis, C. F., Statnikov, E.: Algorithms for large scale markov blanket discovery. In: In The 16th International FLAIRS Conference, St, AAAI Press (2003) 376-380
11. Aliferis, C. F., Tsamardinos, I., Statnikov, E.: Hiton, a novel markov blanket algorithm for optimal variable selection. In: The 2003 American Medical Informatics Association (AMIA) Annual Symposium. (2003) 21-25
12. Ripley, B. D.: Pattern Recognition and Neural Networks. Cambridge University Press, U. K. (1996)
13. Friedman, N., Goldszmidt, M.: Building classifiers using bayesian networks. In: In Proceedings of the thirteenth national conference on artificial intelligence, AAAI Press (1996) 1277-1284
14. Quinlan, J. R.: Induction of decision trees. Machine Learning 1(1) (1986) 81-106
15. Quinlan, J. R.: C4.5: programs for machine learning. Morgan Kaufmann (1993)
16. Sahami, M.: Learning limited dependence bayesian classi-fiers. In: In KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press (1996) 335-338
17. Jensen, F. V.: An introduction to Bayesian networks. Springer, New Work (1996)
18. Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1988)
19. Tsamardinos, I., Brown, L. E., Aliferis, C. F.: The max-min hill-climbing bayesian network structure learning algorithm. Machine Learning 65(1) (2006) 31-78
20. Aliferis, C., Tsamardinos, I.: Algorithms for large-scale local causal discovery and feature selection in the presence of small sample or large causal neighborhoods. In: Technical Report DSL 02-08, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA (2002)
21. Aliferis, C., Tsamardinos, I., Statnikov, A.: Large-scale feature selection using markov blanket induction for the prediction of protein-drug binding. In: Technical Report DSL TR-02-06, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA (2002)
22. Bai, X., Glymour, C., Padman, R., Ramsey, J., Spirtes, P., Wimberly, F.: Pcx: Markov blanket classification for large data sets with few cases. In: CMU-CALD-04-102, School of Computer Science, Carnegie Mellon University (2004)
23. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. Springer, New Work, USA (1993)
24. Garcia-Brazales, E. A.: Discovering the Markov blanket as feature selection oriented to classification task. Master Thesis, Department of Computer Science, Aalborg University, Denmark (2007)
25. Hall, M. A.: Correlation-based Feature Subset Selection for Machine Learning. PhD Thesis, University of Waikato, Hamilton, New Zealand (1998)
26. Binder, J., Russell, S., Smyth, P.: Adaptive probabilistic networks with hidden variables. 29(2) (1997) 213-244
27. Beinlich, I. A., Suermondt, H. J., Chavez, R. M., Cooper, G. F.: The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In: European Conference on Artificial Intelligence in Medicine. (1989) 247-256
28. Geiger, D., Goldszmidt, M., Provan, G.: Bayesian network classifiers. In: Machine Learning. (1997) 131-163