On the Accuracy of Meta-learning for Scalable Data Mining

Journal of Intelligent Information Systems

Volumn 8, Issue 1, 1997, Pages 5-28

  Chan, Philip K ^a   Stolfo, Salvatore J ^b  

^a FLORIDA INSTITUTE OF TECHNOLOGY   (United States)

^b Department of Earth and Environmental Sciences   (United States)

Author keywords

Classifiers; Data mining; Machine learning; Meta learning; Scalability

Indexed keywords

CLASSIFIERS; DATA ACQUISITION; DATABASE SYSTEMS; INFORMATION TECHNOLOGY; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; NEURAL NETWORKS;

DATA MINING; META LEARNING; SCALABILITY;

LEARNING SYSTEMS;

EID: 0030681095     PISSN: 09259902     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008640732416     Document Type: Article

References (23)

1. Ali, K. and Pazzani, M. Error reduction through learning multiple descriptions. Machine Learning, 1996. to appear.
2. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.
3. Buntine, W. and Caruana, R. Introduction to IND and Recursive Partitioning. NASA Ames Research Center, 1991.
4. Catlett, J. Megainduction: A test flight. In Proc. Eighth Intl. Work. Machine Learning, pages 596-599, 1991.
5. Chan, P. and Stolfo, S. Experiments on multistrategy learning by meta-learning. In Proc. Second Intl. Conf. Info. Know. Manag., pages 314-323, 1993.
6. Chan, P. and Stolfo, S. Meta-learning for multistrategy and parallel learning. In Proc. Second Intl. Work. on Multistrategy Learning, pages 150-165, 1993.
7. Chan, P. and Stolfo, S. Toward parallel and distributed learning by meta-learning. In Working Notes AAAI Work. Know. Disc. Databases, pages 227-240, 1993.
8. Chan, P. and Stolfo, S. Scaling learning by meta-learning over disjoint and partially replicated data. In Proc. Ninth Florida AI Research Symposium, pages 151-155, 1996.
9. Clark, P. and Niblett, T. The CN2 induction algorithm. Machine Learning, 3:261-285, 1989.
10. Cost, S. and Salzberg, S. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10:57-78, 1993.
11. Craven, M. and J. Shavlik. Learning to represent codons: A challenge problem for constructive induction. In Proc. IJCAI-93, pages 1319-1324, 1993.
12. Flann, N. and Dietterich, T. A study of explanation-based mehtods for inductive learning. Machine Learning, 4:187-266, 1989.
13. Krogh, A. and Vedelsby, J. Neural network ensembles, cross validation, and active learning. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Info. Proc. Sys. 7, pages 231-238. MIT Press, 1995.
14. Littlestone, N. and Warmuth, M. The weighted majority algorithm. Technical Report UCSC-CRL-89-16, Univ. Cal., Santa Cruz, 1989.
15. Mitchell, T.M. The need for biases in learning generalizaions. Technical Report CBM-TR-117, Dept. Comp. Sci., Rutgers Univ., 1980.
16. Quinlan, J.R. Induction of decision trees. Machine Learning, 1:81-106, 1986.
17. Schapire, R. The strength of weak learnability. Machine Learning, 5:197-226, 1990.
18. Stolfo, S., Galil, Z., McKeown, K., and Mills, R. Speech recognition in parallel. In Proc. Speech Nat. Lang. Work., pages 353-373. DARPA, 1989.
19. Towell, G., Shavlik, J., and Noordewier, M. Refinement of approximate domain theories by knowledge-based neural networks. In Proc. AAAI-90, pages 861-866, 1990.
20. Valiant, L. A theory of the learnable. Comm. ACM, 27:1134-1142, 1984.
21. Wolpert, D. Stacked generalization. Neural Networks, 5:241-259, 1992.
22. Xu, L., Krzyzak, A., and Suen, C. Methods of combining multiple classifies and their applications to handwriting recognition. IEEE Trans. Sys. Man. Cyb., 22:418-435, 1992.
23. Zhang, X., Mesirov, J., and Waltz, D. A hybrid system for protein secondary structure prediction. J. Mol. Biol., 225:1049-1063, 1992.