Abstract
Function decomposition is a promising mechanism for machine learning. This paper investigates its use as a redundancy removal and feature construction preprocessor. Experiments show that its combination with naive Bayesian classifier and decision trees is especially successful on artificial domains while results on real-world data are less encouraging.
This work is a part of the first author’s PhD thesis.
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References
Almuallim, H., and Dietterich, T. G. (1991). Learning with many irrelevant features. In Ninth National Conference on Artificial Intelligence,547–552. MIT Press.
Bloedorn, E., and Michalski, R. S. (1998). Data-driven constructive induction. IEEE Intelligent Systems 13 (2): 30–37.
Craven, M. W., and Shavlik, J. W. (1994). Investigating the value of a good input representation. In Proc. ML-COLT ‘84 Workshop on Constructive Induction and Change of Representation.
Curtis, H. A. (1962). A New Approach to the Design of Switching Functions. Van Nostrand, Princeton, N.J.
Demgar, J., and Zupan, B. (2000). Orange tutorial. Technical report, Artificial Inteligence Laboratory, Faculty of Computer and Information Sciences, Ljubljana.
Demšar, J., Zupan, B., Bohanec, M., and Bratko, I. (1997). Constructing intermediate concepts by decomposition of real functions. In van Someren, M., and Widmer, G., eds., Proc. European Conference on Machine Learning, ECML-97, 93–107. Prague: Springer.
Demšar, J. (1999). Use of function decomposition for attribute space transformation. M. Sc. Thesis (in Slovene).
Dietterich, T. G. (1989). Approximate statistical tests for comparing supervised classification learning algorithms. 10: 1895–1924.
Fayyad, U. M., and 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, 1022–1029. Chambery, France: Morgan-Kaufmann.
John, G. H., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Machine Learning: Proceedings of the Eleventh International Conference, 121–129. San Francisco, CA: Morgan Kaufmann Publishers.
Kohavi, R., and John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence Journal 97: 273–324.
Kohavi, R., and John, G. H. (1998). Wrapper aproach. In Liu, H., and Motoda, H., eds., Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers.
Kramer, S. (1994). CN2-MCI: A two-step method for constructive induction. In Proc. ML-COLT ‘84 Workshop on Constructive Induction and Change of Representation.
Liu, H., and Motoda, H. (1998). Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers.
Murphy, P. M., and Pazzani, M. J. (1991). Id2-of-3: Constructive induction of m-of-n concepts for discriminators in decision trees. In Proceedings of the Eighth International Workshop on Machine Learning, 183–187. Evanston, IL: Morgan-Kaufmann.
Pagallo, G., and Haussier, D. (1990). Boolean feature discovery in empirical learning. Machine Learning 5: 71–99.
Pazzani, M. J. (1998). Constructive induction of cartesian product attributes. In Liu, H., and Motoda, H., eds., Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers.
Ragavan, H., Rendell, L., Shaw, M., and Tessmer, A. (1993). Complex concept acquisition through direct search and feature caching. In Proceedings of Thirteens International Joint Conference on Artificial Inteligence.
Salzberg, S. L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1: 317–328.
Thrun et al., S. B. (1991). A performance comparison of different learning algorithms. Technical report, Carnegie Mellon University CMU-CS-91–197.
Wnek, J., and Michalski, R. S. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning 14: 139–168.
Zupan, B., Bohanec, M., Bratko, I., and Demlar, J. (1997). Machine learning by function decomposition. In D. H. Fisher, J., ed., Proc. Fourteenth International Conference on Machine Learning (ICML-97), 421–429. San Mateo, CA: Morgan Kaufmann.
Zupan, B., Bohanec, M., Demgar, J., and Bratko, I. (1999). Learning by discovering concept hierarchies. Artificial Inteligence Journal.
Zupan, B. (1997). Machine learning based on function decomposition. Ph.D. Dissertation, University of Ljubljana. Available at http://www.ai.ijs.si/BlazZupan/papers.html.
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Demšar, J., Zupan, B., Bratko, I. (2001). Transformation of attribute space by function decomposition. In: Della Riccia, G., Lenz, HJ., Kruse, R. (eds) Data Fusion and Perception. International Centre for Mechanical Sciences, vol 431. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2580-9_12
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DOI: https://doi.org/10.1007/978-3-7091-2580-9_12
Publisher Name: Springer, Vienna
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