A computational environment for extracting rules from databases

Management Information Systems

Volumn 2, Issue , 2000, Pages 321-330

  Baranauskas, J A ^a   Monard, M C ^a   Batista, G E A P A ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA PROCESSING; DATABASE SYSTEMS; FUNCTIONS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; NETWORK PROTOCOLS;

COMPUTATIONAL ENVIRONMENTS; DATA PRE-PROCESSING; DATASETS; GIGABYTES;

DATA MINING;

EID: 4544255386     PISSN: 14706326     EISSN: None     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (25)

1. Baranauskas, J. A. and Monard, M. C. (2000a). Reviewing some machine learning concepts and methods. Technical Report 102, ICMC-USP. ftp://ftp.icmc.sc.usp.br/pub/BIBLIOTECA/rel_tec/Rt_102.ps.zip.
2. Baranauskas, J. A. and Monard, M. C. (2000b). An unified overview of six supervised symbolic machine learning inducers. Technical Report 103, ICMC-USP. ftp://ftp.icmc.sc.usp.br/pub/BIBLIOTECA/rel_tec/Rt_103.ps.zip.
3. Batista, G. E. A. P. A., Carvalho, A. C. P. L., and Monard, M. C. (2000). Applying one-sided selection to unbalanced datasets. In Proceedings of the Mexican Congress on Artificial Intelligence (MICAI), Lecture Notes in Artificial Intelligence. Springer-Verlag. (in print).
4. Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-142.
5. Blake, C., Keogh, E., and Merz, C. J. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html.
6. Breiman, L. (1996a). Arcing classifiers. Technical report, Statistics Department, University of California, ftp://ftp.stat.berkeley.edu/pub/users/ breiman/.
7. Breiman, L. (1996b). Bagging predictors. Machine Learning, 24(2):123-140.
8. Breiman, L. (1996c), Bias, variance and arcing classifiers. Technical Report 460, Statistics Department, University of California.
9. Clark, P. and Boswell, R. (1991). Rule induction with script C sign script N sign 2: Some recent improvements. In Kodratoff, Y., editor, Proceedings of the 5th European Conference (EWSL 91), pages 151-163. Springer-Verlag.
10. Clark, P. and Niblett, T. (1987). Induction in noise domains. In Bratko, I. and Lavrač, N., editors, Proceedings of the Second European Working Session on Learning, pages 11-30, Wilmslow, UK. Sigma.
11. Clark, P. and Niblett, T. (1989). The script C sign script N sign 2 induction algorithm. Machine Learning, 3(4):261-283.
12. Cohen, W. W. (1995). Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, pages 115-123, San Francisco, CA. Morgan Kaufmann.
13. Famili, A., Shen, W.-M., Weber, R., and Simoudis, E. (1997). Data preprocessing and intelligent data analysis. Intelligent Data Analysis, 1(1). http://www.elsevier.com/locate/ida.
14. Freund, Y. and Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the Second European Conference on Computational Learning Theory, pages 23-37. Springer-Verlag.
15. Freund, Y. and Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 123-140, Lake Tahoe, California. Morgan Kaufmann.
16. Kearns, M. J. and Vazirani, U. V. (1994). An Introduction to Computational Learning Theory. Ellis Horwood.
17. Kohavi, R., Sommerfield, D., and Dougherty, J. (1994). ℳℒ script C sign++: A Machine Learning Library in C++. IEEE Computer Society Press.
18. Margineantu, D. D. and Dietterich, T. G. (1997). Pruning adaptive boosting. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 211-218, San Francisco. Morgan Kaufmann.
19. MySQL (2000). The ℳ y script S sign script Q sign ℒ server, http://www.mysql.com.
20. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1:81-106. Reprinted in Shavlik and Dieterich (eds.) Readings in Machine Learning.
21. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1:81-106. Reprinted in Shavlik and Dieterich (eds.) Readings in Machine Learning.
22. Quinlan, J. R. (1988). C4.5 Programs for Machine Learning. Morgan Kaufmann, CA.
23. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2):197-227.
24. Wall, L., Christiansen, T., and Schwartz, R. L. (1996). Programming Perl. O'Reilly & Associates, Inc.
25. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5:241-259.