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A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining

Carvalho, Deborah R. and Freitas, Alex A. (2000) A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining. In: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation. Morgan Kaufmann, San Francisco, California, USA, pp. 1061-1068. ISBN 1-55860-708-0. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:22012)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

The problem of small disjuncts is a serious challenge for data mining algorithms. In essence, small disjuncts are rules covering a small number of examples. Due to their nature, small disjuncts tend to be error prone and contribute to a decrease in predictive accuracy. This paper proposes a hybrid decision tree/genetic algorithm method to cope with the problem of small disjuncts. The basic idea is that examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm, while examples belonging to small disjuncts (whose classification is considerably more difficult) are classified by rules produced by a genetic algorithm specifically designed for this task.

Item Type: Book section
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 09 Sep 2009 13:28 UTC
Last Modified: 16 Feb 2021 12:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/22012 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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