Abstract
This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is evaluated on challenging test configurations. We reached at 96 % success rate on the average together with significant improvement in the speed of convergence.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection, Cambridge. MIT Press, London (1992)
Zhou, C., Xiao, W., Tirpak, T.M., Nelson, P.C.: Evolving Accurate and Compact Classification Rules ith Gene Expression Programming. IEEE Transactions On Evolutionary Computation 7(6), 519–531 (2003)
Bojarczuk, C.C., Lopes, H.S., Freitas, A.A.: Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Engineering in Medicine and Biology Magazine 19(4), 38–44 (2000)
Winkeler, J.F., Manjunath, B.S.: Genetic Programming for Object Detection. In: Proc. Genetic Programming Conference, Stanford, CA (1997)
Smart, W.R., Zhang, M.: Classification Strategies for Image Classification in Genetic Programming. In: Smart, W.R., Zhang, M. (eds.) Proceeding of Image and Vision Computing Conference, Palmerston North, pp. 402–407 (2003)
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(3), 242–258 (2000)
Rodriguez-Dami’an, M., Cernadas, E., S’a-Otero, P.: Pollen Classification Using Brightness-based and Shape-based Descriptors. In: Proceedings of 17th International Conference on Pattern Recognition, Cambridge, vol. 2, pp. 212–215 (2004)
France, I., Duller, A.W.G., Duller, G.A.T., Lamb, H.F.: A New Approach to Automated Pollen Analysis. Quaternary Science Reviews, 537–546 (2000)
France, I., Duller, A.W.G., Lamb, H.F., Duller, G.A.T.: A comparative study of model based and neural network based approaches to automatic pollen identification. In: France, I., Duller, A.W.G., Lamb, H.F., Duller, G.A.T.A. (eds.) British Machine Vision Conference, pp. 340–349 (1997)
Eksin, I., Erol, O.K.: A New Optimization Method: Big-Bang Big-Crunch. In: Advences in Engineering Software. Elsevier, Amsterdam (2005)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (2003)
Duller, A.W.G., Duller, G.A.T., France, I., Lamb, H.F.: A pollen image database for evaluation of automated identification systems. Quaternary Newsletter 89, 4–9 (1999)
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Akyol, A., Yaslan, Y., Erol, O.K. (2007). A Genetic Programming Classifier Design Approach for Cell Images. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_76
Download citation
DOI: https://doi.org/10.1007/978-3-540-75256-1_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75255-4
Online ISBN: 978-3-540-75256-1
eBook Packages: Computer ScienceComputer Science (R0)