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A hybrid genetic algorithm/decision tree approach for coping with unbalanced classes

Carvalho, Deborah R. and Avila, Braulio C. and Freitas, Alex A. (1999) A hybrid genetic algorithm/decision tree approach for coping with unbalanced classes. In: Mackin, N., ed. Proceedings of the 3rd International Conference on the Practical Applications of Knowledge Discovery and Data Mining (PADD-99). The Practical Application Company, London, pp. 61-70. ISBN 19024260405. (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:21853)

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

This paper proposes a new approach for coping wit hthe problem of unbalancaed classes, where some class(es) is(are) much less frequent than other(s). The proposed approach is a hybrid genetic algorithm/decision tree system. The genetic algorithm acts as a wrapper, using the output of a decision tree algorithm (the state-of-the-art C5.0) to compute the fitness of population individuals (candidate solutions to the problem of unbalanced classes). We evaluate the proposed system on a case study application domain about census data.

Item Type: Book section
Uncontrolled keywords: unbalanced classes; decision tree; hybrid genetic algorithm; wrapper; C5.0
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 19 Oct 2009 18:16 UTC
Last Modified: 04 Feb 2020 04:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21853 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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