Rule Induction for Classification Using Multi-Objective Genetic Programming

Reynolds, Alan P. and de la Iglesia, Beatriz (2007) Rule Induction for Classification Using Multi-Objective Genetic Programming. In: Evolutionary Multi-Criterion Optimization 4th International Conference (EMO 2007), 2007-01-01.

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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
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Depositing User: Vishal Gautam
Date Deposited: 18 May 2011 12:58
Last Modified: 11 Apr 2019 14:12
URI: https://ueaeprints.uea.ac.uk/id/eprint/22061
DOI:

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