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Reynolds, Alan P. and de la Iglesia, Beatriz 
ORCID: https://orcid.org/0000-0003-2675-5826
(2007)
Rule Induction for Classification Using Multi-Objective Genetic Programming.
In: Evolutionary Multi-Criterion Optimization 4th International Conference (EMO 2007), 2007-01-01.
Abstract
Multi-objective metaheuristics have previously been applied to partial classification, where the objective is to produce simple, easy to understand rules that describe subsets of a class of interest. While this provides a useful aid in descriptive data mining, it is difficult to see how the rules produced can be combined usefully to make a predictive classifier. This paper describes how, by using a more complex representation of the rules, it is possible to produce effective classifiers for two class problems. Furthermore, through the use of multi-objective genetic programming, the user can be provided with a selection of classifiers providing different trade-offs between the misclassification costs and the overall model complexity.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre |
| Related URLs: | |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 18 May 2011 12:58 |
| Last Modified: | 22 Apr 2023 02:44 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22061 |
| DOI: |
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