Skip to main content
Kent Academic Repository

A Genetic Algorithm-based Solution for the Problem of Small Disjuncts

Carvalho, Deborah R. and Freitas, Alex A. (2000) A Genetic Algorithm-based Solution for the Problem of Small Disjuncts. In: Zighed, Djamel A. and Komorowski, Jan and Zytkow, Jan, eds. rinciples of Data Mining and Knowledge Discovery 4th European Conference. Lecture Notes in Artificial Intelligence . Springer, Berlin, Germany, 217 -233. ISBN 978-3-540-41066-9. E-ISBN 978-3-540-45372-7. (doi:10.1007/3-540-45372-5_35) (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:21900)

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.
Official URL:
http://dx.doi.org/10.1007/3-540-45372-5_35

Abstract

In essence, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. The problem is particularly serious because, although each small disjuncts covers few examples, the set of small disjuncts can cover a large number of examples. This paper proposes a solution to the problem of discovering accurate small-disjunct rules based on genetic algorithms. The basic idea of our method is to use a hybrid decision tree / genetic algorithm approach for classification. More precisely, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm, while examples belonging to small disjuncts are classified by a new genetic algorithm, particularly designed for discovering small-disjunct rules.

Item Type: Book section
DOI/Identification number: 10.1007/3-540-45372-5_35
Uncontrolled keywords: Genetic Algorithm; Accuracy Rate; Predictive Accuracy; Default Rule; Genetic Algorithm Method
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: 01 Oct 2009 19:49 UTC
Last Modified: 16 Nov 2021 10:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21900 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.