Abstract
This paper adopts the idea of discretising continuous attributes (Fayyad and Irani 1993) and applies it to lazy learning algorithms (Aha 1990; Aha, Kibler and Albert 1991). This approach converts continuous attributes into nominal attributes at the outset. We investigate the effects of this approach on the performance of lazy learning algorithms and examine it empirically using both real-world and artificial data to characterise the benefits of discretisation in lazy learning algorithms. Specifically, we have showed that discretisation achieves an effect of noise reduction and increases lazy learning algorithms’ tolerance for irrelevant continuous attributes.
The proposed approach constrains the representation space in lazy learning algorithms to hyper-rectangular regions that are orthogonal to the attribute axes. Our generally better results obtained using a more restricted representation language indicate that employing a powerful representation language in a learning algorithm is not always the best choice as it can lead to a loss of accuracy.
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References
Aha, D. W., Kibler D. & Albert M. K. (1991). Instance-Based Learning Algorithms, Machine Learning 6: 37–66.
Aha, D. W. (1990). A Study of Instance-Based Algorithms for Supervised Learning Tasks: Mathematical, Empirical, and Psychological Evoluations, PhD Thesis, Department of Information and Computer Science, University of California, Irvine, Technical Report 90–42.
Catlett, J. (1991). On Changing Continuous Attributes into Ordered Discrete Attributes. In Kodratoff (ed.) Proceedings of the European Working Session on Learning, pp. 164–178. Springer-Verlag.
Cestnik, B. (1990). Estimating Probabilities: A Crucial Task in Machine Learning. In Proceedings of the European Conference on Artificial Intelligence, 147–149.
Cost, S & Salzberg S. (1993). A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10: 57–78.
Dasarathy, B. V. (ed) (1990). Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press.
Fayyad, U. M. & Irani K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence,1022–1027. Morgan Kaufmann.
Kerber, R. (1992). ChiMerge: Discretization of Numeric Attributes. In Proceedings of the Tenth National Conference on Artificial Intelligence,123–128. AAAI Press/The MIT Press.
Kononenko, I. (1993). Inductive and Bayesian Learning in Medical Diagnosis. Applied Artificial Intelligence 7: 317–337.
Lowe, D. G. (1995). Similarity Metric Learning for a Variable-Kernel Classifier. Neural Computation 7(1) (January): 72–85.
Mooney, R., Shavlik, J., Towell, G. & Gove, A. (1989). An Empirical Comparison of Symbolic and Connectionist Learning Algorithms. In Proceedings of the 11th International Joint Conference on Artificial Intelligence,775–780. Morgan Kaufmann.
Murphy, P. M. (1995). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Department of Information and Computer Science. [http://www.ics.uci.edu/ mlearn/MLRepository.html].
Quinlan, J. R. (1994). Comparing Connectionist and Symbolic Learning Methods. In Hanson, S. J., Drastal, G. A., & Rivest, R. L. (eds.) Computational Learning Theory and Natural Learning Systems, Vol. I, 445–456. The MIT Press.
Rissanen, J. (1989). Stochastic Complexity in Statistical Inquiry. World Scientific.
Schaffer, C. (1994), A Conservation Law for Generalization Performance. In Proceedings of the 11th International Conference on Machine Learning,259–265. Morgan Kaufmann.
Stanfill, C. & Waltz, D. (1986). Toward Memory-Based Reasoning. Communications of the ACM 29 (12): 1213–1228.
Ting, K. M. (1994). Discretization of Continuous-Valued Attributes and Instance-Based Learning. Technical Report 491, Basser Dept of Computer Science, University of Sydney.
Ting, K. M. (1995). Common Issues in Instance-Based and Naive Bayesian Classifiers, PhD Thesis, Basser Department of Computer Science, University of Sydney.
Van de Merckt, T. (1993). Decision Trees in Numerical Attributes Spaces. In Proceedings of the 13th International Joint Conference on Artificial Intelligence,1016–1021. Morgan Kaufmann.
Weiss, S. M. & Kapouleas, I. (1989). An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods. In Proceedings of the 11th International Joint Conference on Artificial Intelligence,781–787. Morgan Kaufmann.
Wettschereck, D. (1994). A Study of Distance-Based Machine Learning Algorithms. PhD Thesis, Department of Computer Science, Oregon State University.
Wong, A. K. C. & Chiu, D. K. Y. (1987). Synthesizing Statistical Knowledge from Incomplete Mixed-mode Data. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9(6): 796–805.
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© 1997 Springer Science+Business Media Dordrecht
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Ting, K.M. (1997). Discretisation in Lazy Learning Algorithms. In: Aha, D.W. (eds) Lazy Learning. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2053-3_6
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DOI: https://doi.org/10.1007/978-94-017-2053-3_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4860-8
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