Avoiding order effects in incremental learning

Authors:
Nicola Di Mauro;Floriana Esposito;Stefano Ferilli;Teresa M. A. Basile
Affiliations:
Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy
Venue:
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Year:
2005

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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.