Abstract
This paper presents a hybrid classifier based on extended Self Organizing Map with Probabilistic Neural Network. In this approach, at first we use feature extraction technique of Self Organizing Map to achieve topological ordering in the input data pattern. Then, with the use of Gaussian function, we obtain a better representation of the input dataset. After that, Probabilistic Neural Network is used to classify the input data. We have tested the proposed scheme on Iris, Glass, Breast Cancer Wisconsin, Wine, Ionosphere, Liver (BUPA), Sonar, Thyroid, and Vehicle data sets. The experimental results show better recognition accuracy of the proposed model than that of traditional Probabilistic Neural Network based classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ibrahiem, M., El Emary, M., Ramakrishnan, S.: On the Application of various probabilistic neural networks in solving different classification problems, vol. 4(6), pp. 772–780. IDOSI Publications (2008)
Rutkowski, L.: Adaptive Probabilistic Neural Networks for Pattern Classification in Time-Varying Environment. IEEE Transactions on Neural Networks 15(4), 811–827 (2004)
Qiakai, N.I., Chao, G., Jing, Y.: Research of Face Image Recognition Based on Probabilistic Neural Networks. In: IEEE 24th Chinese Control and Decision Conference (2012)
Schwenker, F., Kestler, H.A., Palm, G.: Three learning phases for radial-basis-function networks. Neural Networks 14(4-5), 439–458 (2001)
Maglogiannis, I., Sarimveis, H., Kiranoudis, C.T., Chatziioannou, A.A., Oikonomou, N., Aidinis, V.: Radial Basis Function Neural Networks Classification for the Recognition of Idiopathic Pulmonary Fibrosis in Microscopic Images. IEEE Transactions on Information Technology in Biomedicine 12(1), 42–54 (2008)
Anifowose, F.A.: A Comparative Study of Gaussian Mixture Model and Radial Basis Function for Voice Recognition. International Journal of Advanced Computer Science and Applications 1(3) (2010)
Vasighil, M., Kompany-Zareh, M.: Classification Ability of Self Organizing Maps in Comparison with Other Classification Methods. Commun. Math. Comput. Chem. 70, 29–44 (2013)
Wu, S., Chow, T.W.S.: Induction Machine Fault Detection using SOM-Based RBF Neural Networks. IEEE Transactions on Industrial Electronics 51(1), 183–194 (2004)
Chen, L.-H., Ching-Tien, C., Yan-Gu, P.: Groundwater Level Prediction Using SOM-RBFN Multisite Model. Journal of Hydrologic Engineering 15, 624–631 (2010)
Ma, F., Wang, W., Tsang, W.W., Zesheng, T., Shaowei, X., Xin, T.: Probabilistic Segmentation of Volume Data for Visualization Using SOM-PNN Classifier. In: IEEE Symposium on Volume Visualization, pp. 71–78 (1998)
Li-Ye., T., Wei-Peng, L.: Incremental intrusion detecting method based on SOM/RBF. In: Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, pp. 11–14 (2010)
https://www.princeton.edu/~achaney/tmve/wiki100k/docs/Self-organizing_map.html
Specht, D.F.: Probabilistic Neural Networks. Neural Networks 3(1), 109–118 (1990)
http://scistatcalc.blogspot.in/2013/10/wilcoxon-signed-rank-test-calculator.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dey, P., Pal, T. (2015). Extended Self Organizing Map with Probabilistic Neural Network for Pattern Classification Problems. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-11933-5_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11932-8
Online ISBN: 978-3-319-11933-5
eBook Packages: EngineeringEngineering (R0)