Abstract
One of the main drawbacks of many machine learning techniques, such as neural networks or ensemble methods, is the incomprehensibility of the model produced. One possible solution to this problem is to consider the learned model as an oracle and generate a new model that “mimics” the semantics of the oracle by expressing it in the form of rules. In this paper we analyse experimentally the influence of pruning, the size of the invented dataset and the confidence of the examples in order to obtain shorter sets of rules without reducing too much the accuracy of the model. The experiments show that the factors analysed affect the mimetic model in different ways. We also show that by combining these factors in a proper way the quality of the mimetic model improves significantly wrt. other previous reports on the mimetic method.
This work has been partially supported by CICYT under grant TIN 2004-7943-C04-02, Acción Integrada Hispano-Austríaca HU2003-003 and Generalitat Valenciana.
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References
Black, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
Domingos, P.: Knowledge Discovery Via Multiple Models. Intelligent Data Analysis 2(1-4), 187–202 (1998)
Domingos, P.: Learning Multiple Models without Sacrificing Comprehen-sibility. In: Proc. of the 14th National Conf. on AI, p. 829 (1997)
Estruch, V., Ferri, C., Hernandez-Orallo, J., Ramirez-Quintana, M.J.: Simple Mimetic Classifiers. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 156–171. Springer, Heidelberg (2003)
Estruch, V., Hernández-Orallo, J.: Theoretical Issues of Mimetic Classifiers, TR DSIC (2003), http://www.dsic.upv.es/~flip/papers/mim.ps.gz
Provost, F., Domingos, P.: Tree induction for probability-based rankings. Machine Learning 52(3), 199–215 (2003)
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Blanco-Vega, R., Hernández-Orallo, J., Ramírez-Quintana, M.J. (2004). Analysing the Trade-Off Between Comprehensibility and Accuracy in Mimetic Models. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_29
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DOI: https://doi.org/10.1007/978-3-540-30214-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23357-2
Online ISBN: 978-3-540-30214-8
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