Local bagging of decision stumps

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. © Springer-Verlag Berlin Heidelberg 2005.