Rule Induction for Classification Using Multi-Objective Genetic Programming

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.

Full text not available from this repository. (Request a copy)

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
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:

Actions (login required)

View Item View Item