Abstract
All learning algorithms perform very well when provided with a small number of highly relevant features. This paper proposes a constructive induction method to automatically construct such features. The method, named GLOREF (GLObally RElevant Features), exploits low-level interactions between the attributes in order to generate globally relevant features. The usefulness of the approach is demonstrated empirically through a large scale experiment involving 13 classifiers and 24 datasets. Results demonstrate the ability of the method in generating highly informative features and a strong positive effect on the accuracy of the classifiers.
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References
Bloedorn, E., Michalski, R.S.: Data-driven constructive induction. IEEE Intelligent Systems and their Applications 13(2), 30–37 (1998)
Freitas, A.A.: Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Review 16, 177–199 (2001)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, NY (1990)
Hu, Y.-J.: Representational Transformation Through Constructive Induction. PhD thesis, University of California, Irvine (1999)
Jakulin, A., Bratko, I.: Analyzing attribute dependencies. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 229–240. Springer, Heidelberg (2003)
Kononenko, I., Hong, S.J.: Attribute selection for modelling. Future Generation Computer Systems 13, 181–195 (1997)
Langley, P.: Induction of recursive Bayesian classifier. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 152–164. Springer, Heidelberg (1993)
Létourneau, S., Famili, A.F., Matwin, S.: A normalization method for contextual data: Experience from a large-scale application. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 49–54. Springer, Heidelberg (1998)
Murthy, S.K., Kasif, S., Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence Research 2, 1–33 (1994)
Pagallo, G., Haussler, D.: Boolean feature discovery in empirical learning. Machine Learning 5(1), 71–99 (1990)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Rendell, L.A., Seshu, R.: Learning hard concepts through constructive induction: Framework and rationale. Computational Intelligence 6(4), 247–270 (1990)
Vilata, R., Blix, G., Rendell, L.A.: Global data analysis and the fragmentation problem in decision tree induction, pp. 312–326 (1997)
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Létourneau, S., Matwin, S., Famili, A.F. (2008). Generation of Globally Relevant Continuous Features for Classification. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_19
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DOI: https://doi.org/10.1007/978-3-540-68125-0_19
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