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IFIP12
2008

P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction

9 years 25 days ago
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction
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.
Frederic T. Stahl, Max A. Bramer, Mo Adda
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where IFIP12
Authors Frederic T. Stahl, Max A. Bramer, Mo Adda
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