Pruning Classifiers in a Distributed Meta-Learning System

Abstract

JAM is a powerful and portable agent-based distributed data mining system that employs meta-learning techniques to integrate a number of independent classifiers (concepts) derived in parallel from independent and (possibly) inherently distributed databases. Although metalearning promotes scalability and accuracy in a simple and straightforward manner, brute force meta-learning techniques can result in large, inefficient and some times inaccurate meta-classifier hierarchies. In this paper we explore several techniques for evaluating classifiers and we demonstrate that meta-learning combined with certain pruning methods can achieve similar or even better performance results in a much more cost effective manner. Keywords: classifier evaluation, pruning, metrics, distributed mining, meta-learning. This research is supported by the Intrusion Detection Program (BAA9603) from DARPA (F30602-96-1-0311), NSF (IRI-96-32225 and CDA-96-25374) and NYSSTF (423115-445). y Supported in part by IBM..