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A Distributed-Population GA for Discovering Interesting Prediction Rules

Noda, Edgar and Freitas, Alex Alves and Yamakami, Akebo (2002) A Distributed-Population GA for Discovering Interesting Prediction Rules. In: Benitez, J.M. and Gordon, Oscar, eds. Advances in Soft Computing: Engineering Design and Manufacturing. Springer, London, pp. 287-296. ISBN 978-1-84996-905-5. E-ISBN 978-1-4471-3744-3. (doi:10.1007/978-1-4471-3744-3_28) (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:13741)

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)
Official URL
http://dx.doi.org/10.1007/978-1-4471-3744-3_28

Abstract

In data mining, the quality of prediction rules basically involve three criteria: accuracy, comprehensible and interestingness. The majority of the rule induction, literature focuses on discovering accurate, comprehensible rules. In this paper we also take these two criteria into account, but we go beyond them in the sense that we aim at discovering rules that are interesting (surprising) for the user. The search is performed by distributed genetic algorithm (DGA) specifically designed to the discovery of interesting rules.

Item Type: Book section
DOI/Identification number: 10.1007/978-1-4471-3744-3_28
Uncontrolled keywords: genetic algorithms, data mining, classification
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: 24 Nov 2008 17:59 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13741 (The current URI for this page, for reference purposes)
Freitas, Alex Alves: https://orcid.org/0000-0001-9825-4700
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