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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... (Update)
Context of citations to this paper: More
.... for analyzing an ensemble of classifiers (e.g. diversity, correlated error, and coverage) can be used in pruning unnecessary classifiers [16]. More importantly, since thieves also learn and fraud patterns evolve over time, some classifiers are more relevant than others at a...
.... and with respect to the underlying data set (coverage specialty12 based) The pre training pruning algorithms are described in detail in [8]. The post training pruning algorithms are based, the first on a cost complexity pruning technique (a technique used by the CART...
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BibTeX entry: (Update)
Prodromidis, A., Stolfo, S., & Chan, P. (1998). Pruning classifiers in a distributed meta-learning system. Submitted to Data Mining and Knowledge Discovery Journal. http://citeseer.ist.psu.edu/article/prodromidis98pruning.html More
@misc{ prodromidis98pruning,
author = "A. Prodromidis and S. Stolfo and P. Chan",
title = "Pruning classifiers in a distributed meta-learning system",
text = "Prodromidis, A., Stolfo, S., & Chan, P. (1998). Pruning classifiers in
a distributed meta-learning system. Submitted to Data Mining and Knowledge
Discovery Journal.",
year = "1998",
url = "citeseer.ist.psu.edu/article/prodromidis98pruning.html" }
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