Feature selection by Bayesian networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3060, Issue , 2014, Pages 370-379

  Hruschka, Estevam R ^a   Hruschka, Eduardo R ^b   Ebecken, Nelson F F ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b CATHOLIC UNIVERSITY OF SANTOS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA MINING; FEATURE EXTRACTION;

BAYESIAN FEATURE SELECTIONS; BREAST CANCER; CLASSIFICATION RATES; DATA MINING METHODS; FEATURE SUBSET SELECTION; MARKOV BLANKETS; VOTING RECORDS; WISCONSIN;

BAYESIAN NETWORKS;

EID: 7444220224     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24840-8_26     Document Type: Conference Paper

References (20)

1. Guyon, I., Elisseeff, A., An Introduction to Variable and Feature Selection, Journal of Machine Learning Research 3, pp. 1157-1182, 2003.
2. th International Conference on Machine Learning, pp. 284-292, July, 1996.
3. Reunanen, J., Overfitting in Making Comparissons Between Variable Selection Methods, Journal of Machine Learning Research 3, pp. 1371-1382, 2003.
4. Blum, A.L., Langley, P., Selection of Relevant Features and Examples in Machine Learning, Artificial Intelligence, pp. 245-271, 1997.
5. Fayyad, U. M., and Shapiro., G. P., Smyth, P. From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, Fayyad et al. Eds, pp. 1-37, MIT Press, 1996.
6. Bigus, J. P., Data Mining with Neural Networks, First edition, USA, McGraw-Hill, 1996.
7. Han, J. and Kamber, M., Data Mining, Concepts and Techniques. Morgan Kaufmann, 2001.
8. Liu, H. and Motoda, H., Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic, 1998.
9. Yang, Y. and Pederson, J., A comparative study on feature selection in text categorization, Proc. of the Fourteenth International Conference on Machine Learning, 1997.
10. Cheng, J., Bell, D.A., and Liu, W., Learning belief networks from data: An information theory based approach, Proceedings of the sixth ACM International Conference on Information and Knowledge Management, 1997.
11. Quinlan, J. R., C4.5 Program for Machine Learning. Morgan Kaufmann, 1993.
12. Witten, I.H., Frank, E., Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, USA, 2000.
13. Duch, W., Adamczak, R., Grabczewski, K. A New Methodology of Extraction, Optimization and Application of Crisp and Fuzzy Logical Rules, IEEE Transactions on Neural Networks, Vol. 11, n. 2, pp. 1-31, 2000.
14. Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA., 1988.
15. Chickering, D. M., Optimal Structure Identification with Greedy Search, Journal of Machine Learning Research, (3) pp. 507-554, 2002.
16. Hruschka Jr, E. R., Ebecken, N. F. F., Ordering attributes for missing values prediction and data classification. In: Data Mining III - Management Information Systems Series - Vol. 6, ed. Southampton - UK: WIT Press, 2002.
17. Spirtes P., Glymour C., and Scheines R. Causation, Predication, and Search. SpringerVerlag, New York, 1993.
18. Merz, C.J., Murphy, P.M., UCI Repository of Machine Learning Databases, http://www.ics.uci.edu, Irvine, CA, University of California, Department of Information and Computer Science.
19. Cooper G. & Herskovitz, E. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 9, 309-347, 1992.
20. Schllimmer, J.C., Concept acquisition through representational adjustment, Doctoral Dissertation, Department of Information and Computer Science, University of California, Irvine, 1987.