Meta-Learning in Distributed Data Mining Systems: Issues and Approaches (2000)
| Venue: | Advances of Distributed Data Mining |
| Citations: | 71 - 0 self |
BibTeX
@INPROCEEDINGS{Prodromidis00meta-learningin,
author = {Andreas L. Prodromidis and Philip K. Chan and Salvatore J. Stolfo},
title = {Meta-Learning in Distributed Data Mining Systems: Issues and Approaches},
booktitle = {Advances of Distributed Data Mining},
year = {2000},
publisher = {AAAI Press}
}
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Abstract
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some principled fashion multiple classifiers computed separately over different databases. This study, describes meta-learning and presents the JAM system (Java Agents for Meta-learning), an agent-based meta-learning system for large-scale data mining applications. Specifically, it identifies and addresses several important desiderata for distributed data mining systems that stem from their additional complexity co...
