Local bagging of decision stumps

Authors:
S. B. Kotsiantis;G. E. Tsekouras;P. E. Pintelas
Affiliations:
Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece;Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece;Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

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Abstract

Local methods have significant advantages when the probability measure defined on the space of symbolic objects for each class is very complex, but can still be described by a collection of less complex local approximations. We propose a technique of local bagging of decision stumps. We performed a comparison with other well known combining methods using the same base learner, on standard benchmark datasets and the accuracy of the proposed technique was greater in most cases.