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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Reynolds, A. P., & De La Iglesia, B. (2007). Rule induction for classification using multi-objective genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4403 LNCS, pp. 516–530). Springer Verlag. https://doi.org/10.1007/978-3-540-70928-2_40
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