Academic Commons

Reports

Pruning Classifiers in a Distributed Meta-Learning System

Prodromidis, Andreas L.; Stolfo, Salvatore

JAM is a powerful and portable agent-based distributed data mining system that employs meta-learning 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 their 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.

Subjects

Files

More About This Work

Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-011-98
Published Here
April 25, 2011
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.