Training Product-Unit Neural Networks with Cuckoo Optimization Algorithm for Classification
DOI:
https://doi.org/10.18201/ijisae.2017533900Keywords:
ANN, Classification, Cuckoo algorithm, PUNN.Abstract
In this study Product-Unit Neural Networks (PUNN) which is the special class of feed-forward neural network, has been trained using Cuckoo Optimization algorithm. The trained model has been applied to two classification problem. BUPA liver disorders and Haberman's Survival Data have been used for application. The both data have been obtained from UCI machine Learning Repository. For comparison Backpropagation (BP) and Levenberg–Marquardt (LM) algorithms have been used. The application results show that the PUNN trained with Cuckoo Optimization algorithm is achieved better classification accuracy.
Downloads
References
S. Alwaisi, Ö. K. Baykan, “Training Of Artificial Neural Network Using Metaheuristic Algorithm”, International Journal of Intelligent Systems and Applications in Engineering, IJISAE, Special Issue, 12–16.
S. Mukhopadhyay, C. Tang, J. Huang, M. Yu, M. Palakal, “A comparative study of genetic sequence classification algorithms”, Neural networks for signal processing. In Proceedings of the 2002 12th IEEE workshop on 4–6 September 2002, pp. 57–66.
C. Hervás, F. J. Martínez, P. A. Gutiérrez, “Classification by means of Evolutionary Product-Unit Neural Networks”, 2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006.
de N. Arie, M. Erwin, S. Sebastiaan, "Accurate Prediction Of Ecological Quality Ratio With Product Unit Neural Networks" , CUNY Academic Works, 2014.
A. Guerrero-Enamorado, D. Ceballos-Gastell, “An Experimental Study of Evolutionary Product-Unit Neural Network Algorithm”, Computación y Sistemas, Vol. 20, No. 2, 2016, pp. 205–218, doi: 10.13053/CyS-20-2-2218
C. Zhang, W. Wu, X. H. Chen, Y. Xiong, “Convergence of BP algorithm for product unit neural networks with exponential, weights, Neurocomputing”, Volume 72, Issues 1–3, Pp 513-520, 2008.
A. Martı´nez-Estudillo, F. Martı´nez-Estudillo, C. Herva´s-Martı´nez, N. Garcı´a-Pedrajas, “Evolutionary product unit based neural networks for regression”, Neural Networks 19, 477–486, 2006.
K. Dulakshi, A. W. Jayawardena and W. K. Li, “Evolutionary product unit based neural networks for hydrological time series analysis”, Journal of Hydroinformatics, 13.4, 2011.
F.J. Martı´nez-Estudillo, C. Herva´s-Martı´nez, P. A. Gutie´rrez, A. C. Martı´nez-Estudillo, “Evolutionary product-unit neural networks classifiers”, Neurocomputing 72, 548–561, 2008.
R. Rajabioun, “Cuckoo Optimization Algorithm”, Applied Soft Computing, Volume 11, Issue 8, pp. 5508-5518, 2011.
M. Lichman, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, last accessed 15.08.2017.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.