Abstract
A new genetic programming based approach to classification problems is proposed. Differently from other approaches, the number of prototypes in the classifier is not a priori fixed, but automatically found by the system. In fact, in many problems a single class may contain a variable number of subclasses. Hence, a single prototype, may be inadequate to represent all the members of the class. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Cordella, L. P., De Stefano, C., Fontanella, F., & Marcelli, A. (2005). A novel genetic programming based approach for classification problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3617 LNCS, pp. 727–734). https://doi.org/10.1007/11553595_89
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