Hybrid algorithms with instance-based classification

Authors:
Iris Hendrickx;Antal van den Bosch
Affiliations:
ILK / Computational Linguistics and AI, Tilburg University, Tilburg, The Netherlands;ILK / Computational Linguistics and AI, Tilburg University, Tilburg, The Netherlands
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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Abstract

In this paper we aim to show that instance-based classification can replace the classifier component of a rule learner and of maximum-entropy modeling, thereby improving the generalization accuracy of both algorithms. We describe hybrid algorithms that combine rule learning models and maximum-entropy modeling with instance-based classification. Experimental results show that both hybrids are able to outperform the parent algorithm. We analyze and compare the overlap in errors and the statistical bias and variance of the hybrids, their parent algorithms, and a plain instance-based learner. We observe that the successful hybrid algorithms have a lower statistical bias component in the error than their parent algorithms; the fewer errors they make are also less systematic.