BibTeX
@MISC{98pruningmeta-classifiers,
author = {},
title = {Pruning Meta-Classifiers in a Distributed Data Mining System ∗},
year = {1998}
}
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Abstract
JAM is a powerful and portable agent-based distributed data mining system that employs metalearning techniques to integrate a number of independent classifiers (models) derived in parallel from independent and (possibly) inherently distributed databases. Although meta-learning promotes scalability and accuracy in a simple and straightforward manner, brute force meta-learning techniques can result in large, redundant, inefficient and some times inaccurate meta-classifier hierarchies. In this paper we explore several methods for evaluating classifiers and composing meta-classifiers, we expose ther limitations and we demonstrate that meta-learning combined with certain pruning methods has the potential to achieve similar or even better performance results in a much more cost effective manner.
