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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Cordella, L.P., De Stefano, C., Fontanella, F., Marcelli, A. (2005). A Novel Genetic Programming Based Approach for Classification Problems. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_89
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DOI: https://doi.org/10.1007/11553595_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
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