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.
Chapter PDF
References
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & sons, Inc., Chichester (2001)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. Statistics and Computing 4 (1994)
Bastian, A.: Identifying fuzzy models utilizing genetic programming. Fuzzy Sets and Systems 113, 333–350 (2000)
Koppen, M., Nickolay, B.: Genetic programming based texture filtering framework. Pattern recognition in soft computing paradigm, 275–304 (2001)
Agnelli, D., Bollini, A., Lombardi, L.: Image classification: an evolutionary approach. Pattern Recognition Letters 23, 303–309 (2002)
Rauss, P.J., Daida, J.M., Chaudhary, S.A.: Classification of spectral image using genetic programming. In: GECCO, pp. 726–733 (2000)
Kishore, J.K., Patnaik, L.M., Mani, V., Agrawal, V.K.: Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation 4, 242–258 (2000)
Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. IEEE Trans. Evolutionary Computation 8, 183–196 (2004)
Blickle, T., Thiele, L.: A comparison of selection schemes used in genetic algorithms. Technical Report 11, Gloriastrasse 35, 8092 Zurich, Switzerland (1995)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11553595_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
Online ISBN: 978-3-540-31866-8
eBook Packages: Computer ScienceComputer Science (R0)