Abstract
In this paper, wedescribe a general approach to scaling data mining applications thatwe have come to call meta-learning. Meta-Learningrefers to a general strategy that seeks to learn how to combine anumber of separate learning processes in an intelligent fashion. Wedesire a meta-learning architecture that exhibits two key behaviors.First, the meta-learning strategy must produce an accurate final classification system. This means that a meta-learning architecturemust produce a final outcome that is at least as accurate as aconventional learning algorithm applied to all available data.Second, it must be fast, relative to an individual sequential learningalgorithm when applied to massive databases of examples, and operatein a reasonable amount of time. This paper focussed primarily onissues related to the accuracy and efficacy of meta-learning as ageneral strategy. A number of empirical results are presenteddemonstrating that meta-learning is technically feasible in wide-area,network computing environments.
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Ali, K. and Pazzani, M. Error reduction through learning multiple descriptions. Machine Learning, 1996. to appear.
Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.
Buntine, W. and Caruana, R. Introduction to IND and Recursive Partitioning. NASA Ames Research Center, 1991.
Catlett, J. Megainduction: A test flight. In Proc. Eighth Intl. Work. Machine Learning, pages 596–599, 1991.
Chan, P. and Stolfo, S. Experiments on multistrategy learning by meta-learning. In Proc. Second Intl. Conf. Info. Know. Manag., pages 314–323, 1993.
Chan, P. and Stolfo, S. Meta-learning for multistrategy and parallel learning. In Proc. Second Intl. Work. on Multistrategy Learning, pages 150–165, 1993.
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.
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.
Clark, P. and Niblett, T. The CN2 induction algorithm. Machine Learning, 3:261–285, 1989.
Cost, S. and Salzberg, S. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10:57–78, 1993.
Craven, M. and J. Shavlik. Learning to represent codons: A challenge problem for constructive induction. In Proc. IJCAI-93, pages 1319–1324, 1993.
Flann, N. and Dietterich, T. A study of explanation-based mehtods for inductive learning. Machine Learning, 4:187–266, 1989.
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.
Littlestone, N. and Warmuth, M. The weighted majority algorithm. Technical Report UCSC-CRL-89-16, Univ. Cal., Santa Cruz, 1989.
Mitchell, T.M. The need for biases in learning generalizaions. Technical Report CBM-TR-117, Dept. Comp. Sci., Rutgers Univ., 1980.
Quinlan, J.R. Induction of decision trees. Machine Learning, 1:81–106, 1986.
Schapire, R. The strength of weak learnability. Machine Learning, 5:197–226, 1990.
Stolfo, S., Galil, Z., McKeown, K., and Mills, R. Speech recognition in parallel. In Proc. Speech Nat. Lang. Work., pages 353–373. DARPA, 1989.
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.
Valiant, L. A theory of the learnable. Comm. ACM, 27:1134–1142, 1984.
Wolpert, D. Stacked generalization. Neural Networks, 5:241–259, 1992.
Xu, L., Krzyzak, A., and Suen, C. Methods of combining multiple classifires and their applications to handwriting recognition. IEEE Trans. Sys. Man. Cyb., 22:418–435, 1992.
Zhang, X., Mesirov, J., and Waltz, D. A hybrid system for protein secondary structure prediction. J. Mol. Biol., 225:1049–1063, 1992.
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Chan, P.K., Stolfo, S.J. On the Accuracy of Meta-learning for Scalable Data Mining. Journal of Intelligent Information Systems 8, 5–28 (1997). https://doi.org/10.1023/A:1008640732416
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DOI: https://doi.org/10.1023/A:1008640732416