Rule Induction for Classification Using Multi-objective Genetic Programming

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.

• Type: Conference
• Year: 2006
• Where: EMO
• Authors: Alan P. Reynolds, Beatriz de la Iglesia
• Keywords: Descriptive Data Mining, EMO 2006, Multi-objective Metaheuristics, Optimization, Partial Classification