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. | |
| 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 |
| Funders: | Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (https://ror.org/04hr99439) |
| Depositing User: | Mark Wheadon |
| Date Deposited: | 24 Nov 2008 17:59 UTC |
| Last Modified: | 05 Nov 2024 09:47 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/13741 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Freitas, Alex Alves.
| Creator's ORCID: |
 https://orcid.org/0000-0001-9825-4700
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