Rule Induction for Classification Using Multi-Objective Genetic Programming

Alan P. Reynolds, Beatriz de la Iglesia

Research output: Contribution to conferencePaper

13 Citations (Scopus)

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.
Original languageEnglish
Pages516-530
Number of pages15
Publication statusPublished - 2007
EventEvolutionary Multi-Criterion Optimization 4th International Conference (EMO 2007) - Matsushima, Japan
Duration: 1 Jan 2007 → …

Conference

ConferenceEvolutionary Multi-Criterion Optimization 4th International Conference (EMO 2007)
CountryJapan
CityMatsushima
Period1/01/07 → …

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