Abstract
The paper presents a feature extraction method for improving pattern classification efficiency of the radial basis function neural network. The principal component analysis in combination with preprocessing by vector autoscaling and dimensional autoscaling has been used to generate two alternate feature vector representations of the objects. A feature fusion scheme is proposed in which the two feature sets are combined by simple concatenation and then allowed to undergo genetic evolution. The fused features are obtained by applying a weighting method based on the prevalence of feature components in the terminal population. The present method of feature extraction in combination with radial basis neural network has been demonstrated to improve the classification rate for nine benchmark datasets analyzed.
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References
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, ch. 1. Academic, San Diego (2003)
Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)
Jain, A.K.: Statistical Pattern Recognition: A Review. IEEE Trans. Pattern Anal Machine Intelligence 22(1), 4–37 (2000)
Gutierrez-Osuna, R.: Pattern Analysis for Machine Olfaction: A Review. IEEE Sensors J. 2(3), 189–202 (2002)
Osuna, R.G., Nagle, H.T.: A Method for Evaluating Data Preprocessing Techniques for Odor Classification with an Array of Gas Sensors. IEEE Trans. Syst. Man Cybern. B 29(5), 626–632 (1999)
Somvanshi, D., Yadava, R.D.S.: Boosting Principal Component Analysis by Genetic Algorithm. Defence Science Journal 60, 392–398 (2010)
Zohdy, M.A., Loh, N., Liu, J.: Application of Maximum Likelihood Identification with Multisensor Fusion to Stochastic Systems. In: Proceedings of American Control Conference, Pennsylvania, pp. 411–416 (1989)
Khan, A.A., Zohdy, M.A.: A Genetic Algorithm for Selection of Noisy Sensor Data in Multisensor Data Fusion. In: Proceedings of American Control Conference, Albuquerque, pp. 2256–2262 (1997)
Cover, T.M., Thomas, J.A.: Elements of Information Theory, Section 2.1. Wiley & Sons, Hoboken (2006)
Pardo, M., Sberveglieri, G.: Coffee Analysis with an Electronic Nose. IEEE Trans. Instrum. Measur. 51, 1334–1339 (2002), http://sensor.ing.unibs.it/_people/pardo/dataset.html
Rose-Pehrson, S.L., Lella, D.D., Grate, J.W.: Smart Sensor System and Method Using Surface Acoustic Wave Vapor Sensor Array and Pattern Recognition for Selective Trace Organic Vapor Detection. U.S. Patent 5469369 (1995)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA, http://archive.ics.uci.edu/ml/datasets.html
Jha, S.K., Yadava, R.D.S.: Denoising by Singular Value Decomposition and Its Application to Electronic Nose Data Processing. IEEE Sensors J. 11, 35–44 (2011)
Ling, C.: Stream data classification using improved fisher discriminate analysis. J. Computers 4, 208–214 (2009)
Moradian, M., Baraani, A.: KNNBA: K- Nearest-Neighbor Based-Association Algorithm. J. Theor. Appl. Informat. Technol. 6, 123–129 (2009)
Autio, L., Juhola, M., Laurikkala, J.: On the Neural Network Classification of Medical Data and an Endeavor to Balance Non-Uniform Data Sets with Artificial Data Extension. Computers in Biology and Medicine 37, 388–397 (2007)
Frank, E., Hall, M.: A Simple Approach to Ordinal Classification. In: De Raedt, L.H., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 145–156. Springer, Heidelberg (2001)
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Verma, P., Yadava, R.D.S. (2011). Genetic Algorithm Assisted Enhancement in Pattern Recognition Efficiency of Radial Basis Neural Network. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_32
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DOI: https://doi.org/10.1007/978-3-642-27172-4_32
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