Abstract
This paper addresses the problem of mitigating the order effects in incremental learning, a phenomenon observed when different ordered sequences of observations lead to different results. A modification of an ILP incremental learning system, with the aim of making it order-independent, is presented. A backtracking strategy on theories is incorporated in its refinement operators, which causes a change of its refinement strategy and reflects the human behavior during the learning process. A modality to restore a previous theory, in order to backtrack on a previous knowledge level, is presented. Experiments validate the approach in terms of computational cost and predictive accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Langley, P.: Order effects in incremental learning. In: Reimann, P., Spada, H. (eds.) Learning in humans and machines: Towards an Interdisciplinary Learning Science. Elsevier, Amsterdam (1995)
Di Mauro, N., Esposito, F., Ferilli, S., Basile, T.A.: A backtracking strategy for order-independent incremental learning. In: de Mantaras, R.L. (ed.) Proceedings of ECAI 2004. IOS Press, Amsterdam (2004)
Mitchell, T.: Generalization as search. Artificial Intelligence 18, 203–226 (1982)
Cornuéjols, A.: Getting order independence in incremental learning. In: Brazdil, P.B. (ed.) ECML 1993. LNCS(LNAI), vol. 667, pp. 196–212. Springer, Heidelberg (1993)
Talavera, L., Roure, J.: A buffering strategy to avoid ordering effects in clustering. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 316–321. Springer, Heidelberg (1998)
Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 139–172 (1987)
Utgoff, P.E.: Incremental induction of decision trees. Machine Learning 4, 161–186 (1989)
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Conceptual change in learning naive physics: The computational model as a theory revision process. In: Lamma, E., Mello, P. (eds.) AI*IA 1999. LNCS (LNAI), vol. 1792, pp. 214–225. Springer, Heidelberg (1999)
Esposito, F., Ferilli, S., Fanizzi, N., Basile, T., Di Mauro, N.: Incremental multistrategy learning for document processing. Applied Artificial Intelligence Journal 17, 859–883 (2003)
Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., Ferilli, S.: A logic framework for the incremental inductive synthesis of datalog theories. In: Fuchs, N.E. (ed.) LOPSTR 1997. LNCS, vol. 1463, pp. 300–321. Springer, Heidelberg (1998)
Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS(LNAI), vol. 1228. Springer, Heidelberg (1997)
Esposito, F., Laterza, A., Malerba, D., Semeraro, G.: Locally finite, proper and complete operators for refining datalog programs. In: Michalewicz, M., Rás, Z.W. (eds.) ISMIS 1996. LNCS(LNAI), vol. 1079, pp. 468–478. Springer, Heidelberg (1996)
Michalski, R.S.: Knowledge repair mechanisms: Evolution vs. revolution. In: Proceedings of ICML 1985, Skytop, PA, pp. 116–119 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Mauro, N., Esposito, F., Ferilli, S., Basile, T.M.A. (2005). Avoiding Order Effects in Incremental Learning. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_12
Download citation
DOI: https://doi.org/10.1007/11558590_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29041-4
Online ISBN: 978-3-540-31733-3
eBook Packages: Computer ScienceComputer Science (R0)