Abstract
In this paper we compare the performance of KNOMA (Knowledge Mining Approach), a meta-learning approach for integration of rule-based classifiers, based on different rule inducers. Meta-learning approaches use a core learning algorithm for the generation of base classifiers that are further combined into a global one. This approach improves performance and scalability of data mining processes on large datasets. In a previous work we presented KNOMA, a meta-learning approach whose performance was evaluated using RIPPER as its core learning algorithm. Experiments have shown that the performance of KNOMA is comparable to that achieved with Bagging and Boosting. However, meta-learning is generally only sensitive to core algorithms used in the generation of base classifiers. KNOMA is a generic approach and can handle different rule-based inducers, although its advantages, drawbacks and use cases need to be precisely identified. We studied the variation of performance in the approach with base classifiers generated by two rule inducers (C45Rules and RIPPER) and also by C4.5. Interesting behaviors have been noticed in the experiments.
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References
Bernardini, F.C., Monard, M.C.: Methods for Constructing Symbolic Ensembles from Symbolic Classifiers. In: Congress of Logic Applied to Technology, 2005, Hyogo. Frontiers in AI and Applications, vol. 132, pp. 161–168. IOS Press, Netherlands (2005)
Bernardini, F.C., Monard, M.C., Prati, R.C.: Constructing Ensembles of Symbolic Classifiers. In: International Conference on Hybrid Intelligent Systems, 2005, Rio de Janeiro. Proceedings of the Fifth International Conference on Hybrid Intelligent Systems, vol. 1, pp. 315–320. IEEE Computer Society, California (2005)
Blake. C., Merz, C.J.: UCI Repository of machine learning databases. Department of Information and Computer Science. University of California, Irvine (1998), www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Chan, P.K., Stolfo, S.J.: Experiments on multistrategy learning by meta-learning. In: Proc. of the 2th International Conference on Information and knowledge management table of contents, Washington, D.C., United States, pp. 314–323 (1993) ISBN 0-89791-626-3
Chan, P.K., Stolfo, S.J.: A comparative evaluation of voting and meta-learning on partitioned data. In: Proc. 12th Int. Conf. on Machine Learning, pp. 90–98 (1995)
Chan, P.K., Stolfo, S.J.: Learning arbiter and combiner trees from partitioned data for scaling machine learning. In: Proc. First Int. Conf. Knowledge Discovery and Data Mining (KDD 1995), pp. 39–44 (1995a)
Cohen, W.W.: Fast effective rule induction. In: Proc. 20th. International Conference on Machine Learning, pp. 115–123. Morgan Kauffman, San Francisco (1995)
Enembreck, F., Ávila, B.C.: KNOMA: A new Approach for Knowledge Integration. In: 11th. IEEE Int. Symposium on Computers and Communications, Italy (2006) (to appear)
Freitas, A.A., Lavington, S.H.: Mining very large databases with Parallel Processing. Kluwer Academic Publishers, The Netherlands (1998) ISBN 0-7923-8048-7
Freund, Y., Shapire, R.E.: Experiments with a New Boosting Algorithm. In: Proc. of International Conference on Machine Learning, pp. 148–156 (1996)
Kurgan, L.: Reducing Complexity of Rule Based Models via Meta Mining. In: The 2004 Int. Conference on Machine Learning and Applications, ICMLA 2004 (2004)
Prodromidis, A., Chan, P., Stolfo, S.: Meta-learning in distributed data mining systems: Issues and approaches. In: Kargupta, H., Chan, P. (eds.) Advances in Distributed and Parallel Knowledge Discovery, AAAI/MIT Press (2000)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kauffman Publishers, San Francisco (1993)
Shapire, R.E.: The Boosting Approach to Machine Learning: An Overview. In: MSRI Worshop on Nonlinear Estimation and Classification (2002)
Ting, K.M., Witten, I.H.: Stacking Bagged and Dagged Models. In: ICML 1997, pp. 367–375 (1997)
Witten, I.H., Frank, E.: Data Mining. Morgan Kauffman Publishers, San Francisco (2000) ISBN 1-55860-552-5
Wolpert, D.H.: Stacked Generalization. Neural Networks (5), 241–259 (1992)
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Enembreck, F., Ávila, B.C. (2006). Comparing Meta-learning Algorithms. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_33
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DOI: https://doi.org/10.1007/11874850_33
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