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P-Prism: a computationally efficient approach to scaling up classification rule induction

Stahl, F., Bramer, M. and Adda, M. (2008) P-Prism: a computationally efficient approach to scaling up classification rule induction. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice II. IFIP – The International Federation for Information Processing (276). Springer, USA, pp. 77-86. ISBN 9780387096940

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To link to this item DOI: 10.1007/978-0-387-09695-7_8

Abstract/Summary

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30151
Additional Information:Proc. of the IFIP 2008 AI Stream, 20th World Computer Congress
Publisher:Springer

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