Abstract
Constructive induction is the process of changing the representation of examples by creating new attributes from existing attributes. In classification, the goal of constructive induction is to find a representation that facilitates learning a concept description by a particular learning system. Typically, the new attributes are Boolean or arithmetic combinations of existing attributes and the learning algorithms used are decision trees or rule learners. We describe the construction of new attributes that are the Cartesian product of existing attributes. We consider the effects of this operator on three learning algorithms and compare two different methods for determining when to construct new attributes with this operator
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References
Aha, D. & Bankert, R. (1995). A Comparative evaluation of sequential feature selection algorithms. Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics. Ft. Lauderdale, Fl.
Almuallim, H., and Dietterich, T. G. (1991). Learning with many irrelevant features. In Ninth National Conference on Artificial Intelligence, 547–552. MIT Press.
Caruana, R., & Freitag, D. (1994). Greedy attribute selection. In Cohen, W., and Hirsh, H., eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.
Cost, S. & Salzberg, S. (1993). A weighted nearest neighbor algorithm for learning with symbolic features Machine Learning, 10, 57–78.
Danyluk, A. & Provost, F. (1993). Small disjuncts in action: Learning to diagnose errors in the telephone network local loop. Machine Learning Conference, pp 81–88.
Dougherty, J., Kohavi, J., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. Machine Learning Conference, pp 194–202.
Duda, R. & Hart, P. (1973). Pattern classification and scene analysis. New York: John Wiley & Sons.
John, G. Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. Proceedings of the Eleventh International Conference on Machine Learning. New Brunswick, NJ.
Kittler, J. (1986). Feature selection and extraction. In Young & Fu, (eds.), Handbook of pattern recognition and image processing. New York: Academic Press.
Kohavi, R. (1995). Wrappers for performance enhancement and oblivious decision graphs. Ph.D. dissertation. Stanford University.
Kononenko, I. (1990). Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition. In B. Wielinga (Eds.), Current trends in knowledge acquisition. Amsterdam: IOS Press.
Kononenko, I. (1991). Semi-naive Bayesian classifier. Proceedings of the Sixth European Working Session on Learning. (pp. 206–219). Porto, Portugal: Pittman.
Langley, P. (1993). Induction of recursive Bayesian classifiers. Proceedings of the 1993 European Conference on Machine Learning. (pp. 153–164). Vienna: Springer-Verlag.
Langley, P. & Sage, S. (1994). Induction of selective Bayesian classifiers. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Seattle, WA.
Matheus C.J., & Rendell L.A. (1989). Constructive Induction On Decision Trees, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Los Altos, CA, 645–650.
Murphy, P. M., & Aha, D. W. (1995). UCI Repository of machine learning databases. Irvine: University of California, Department of Information & Computer Science.[Machine-readable data repository ftp://www.ics.uci.edu/pub/machine-learning-databases/
Murthy, S. & Salzberg, S. (1995). Lookahead and Pathology in Decision Tree Induction Proceedings of the International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Los Altos, CA, 1025–1031.
Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
Rachlin, Kasif, Salzberg & Aha, (1994). Towards a better understanding of memory-based reasoning systems. Proceedings of the Eleventh Conference on Machine Learning. New Brunswick, NJ
Ragavan, H. & Rendell, L. (1993). Lookahead feature construction for l earning hard concepts. Machine Learning: Proceedings of the Tenth International Conference. Morgan Kaufmann
Salzberg, S., Chandar, R., Ford, H., Murthy, S. & White, R. (1995). Decision Trees for Automated Identification of Cosmic Ray Hits in Hubble Space Telescope Images. Publications of the Astronomical Society of the Pacific.
Sanger T.D., Sutton R.S., & Matheus C.J.(1992). Iterative Construction of Sparse Polynomial Approximations, in Moody J.E., et al.(eds.), Neural Information Processing Systems 4, Morgan Kaufmann, San Mateo, CA, pp.1064–1071.
Stanfill, C. & Waltz, D. (1986). Towards memory-based reasoning. Communications of the ACM, 29, 1213–1228.
Wan, S. & Wong S. (1989). A measure for concept dissimilarity and its application in Machine Learning. In R. Janicki and W. Koczkodaj (eds.) Computing and Information 267–274.
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Pazzani, M.J. (1998). Constructive Induction of Cartesian Product Attributes. In: Liu, H., Motoda, H. (eds) Feature Extraction, Construction and Selection. The Springer International Series in Engineering and Computer Science, vol 453. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5725-8_21
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DOI: https://doi.org/10.1007/978-1-4615-5725-8_21
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