1998 Reports
Pruning Classifiers in a Distributed Meta-Learning System
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.
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- cucs-011-98.pdf application/pdf 182 KB Download File
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