Pappa, Gisele L. and Freitas, Alex A. and Kaestner, Celso A.A. (2002) A multiobjective genetic algorithm for attribute selection. In: Lofti, Ahmed and Garibaldi, Jon and John, Robert, eds. Proceedings Of The 4th International Conference On Recent Advances In Soft Computing. Nottingham Trent University, pp. 116-121. ISBN 1-84233-076-4. (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:13687)
| 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. |
Abstract
The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach. The algorithm’s main goal is to find the best subset of features that minimizes both the error rate and the size of the tree discovered by a classification algorithm, namely C4.5, using the Pareto dominance concept
| Item Type: | Book section |
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| Uncontrolled keywords: | attribute selection, data mining, multiobjective genetic algorithm |
| 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: | 05 Nov 2024 09:47 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/13687 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Freitas, Alex A..
| Creator's ORCID: |
 https://orcid.org/0000-0001-9825-4700
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