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Pruning Meta-Classifiers in a Distributed Data Mining System (1998)
| Venue: | In In Proc of the First National Conference on New Information Technologies |
| Citations: | 13 - 5 self |
BibTeX
@INPROCEEDINGS{Prodromidis98pruningmeta-classifiers,
author = {Andreas L. Prodromidis and Salvatore J. Stolfo},
title = {Pruning Meta-Classifiers in a Distributed Data Mining System},
booktitle = {In In Proc of the First National Conference on New Information Technologies},
year = {1998},
pages = {151--160}
}
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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 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. 1 Introduction Machine learning constitutes a significant part in the overall Knowledge Discovery and Data Mining (KDD) process, the process of extracting useful knowledge from large databases. One...
Keyphrases
distributed data mining system time inaccurate meta-classifier hierarchy brute force meta-learning technique overall knowledge discovery introduction machine useful knowledge cost effective manner independent classifier straightforward manner data mining system data mining several method meta-learning promotes scalability performance result significant part large database
