Abstract
As mentioned in Chapter 5, the orthogonal decision boundaries of fully complex valued neural networks help them to perform classification tasks efficiently. Therefore, in this chapter, we study the classification performance of FC-MLP and ICMLP described in Chapter 2, FC-RBF and Mc-FCRBF explained in Chapter 3, FCRN and CC-ELM described in the chapters 5 and 6 respectively. First, the study is conducted on a set of benchmark real-valued classification problems from the UCI machine learning repository [1] and then, using a practical acoustic emission signal classification problem for health monitoring [2].
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
References
Blake, C., Merz, C.: UCI repository of machine learning databases. Department of Information and Computer Sciences. University of California, Irvine (1998), http://archive.ics.uci.edu/ml/
Omkar, S.N., Suresh, S., Raghavendra, T.R., Mani, V.: Acoustic emission signal classification using fuzzy C-means clustering. In: Proc. of the ICONIP 2002, 9th International Conference on Neural Information Processing, vol. 4, pp. 1827–1831 (2002)
Suresh, S., Sundararajan, N., Saratchandran, P.: Risk-sensitive loss functions for sparse multi-category classification problems. Information Sciences 178(12), 2621–2638 (2008)
Nitta, T.: The Computational Power of Complex-Valued Neuron. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 993–1000. Springer, Heidelberg (2003)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Yingwei, L., Sundararajan, N., Saratchandran, P.: Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Transactions on Neural Networks 9(2), 308–318 (1998)
Huang, G.B., Saratchandran, P., Sundararajan, N.: An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 34(6), 2284–2292 (2004)
Liang, N.-Y., Huang, G.B., Saratchandran, P., Sundararajan, N.: A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 17(6), 1411–1423 (2006)
Suresh, S., Venkatesh Babu, R., Kim, H.J.: No-reference image quality assessment using modified extreme learning machine classifier. Applied Soft Computing 9(2), 541–552 (2009)
Suresh, S., Sundararajan, N., Saratchandran, P.: A sequential multi-category classifier using radial basis function networks. Neurocomputing 71(7-9), 1345–1358 (2008)
Suresh, S., Dong, K., Kim, H.J.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16–18), 3012–3019 (2010)
Amin, M.F., Islam, M.M., Murase, K.: Ensemble of single-layered complex-valued neural networks for classification tasks. Neurocomputing 72(10-12), 2227–2234 (2009)
Amin, M.F., Murase, K.: Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing 72(4-6), 945–955 (2009)
Aizenberg, I., Moraga, C.: Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Computing 11(2), 169–183 (2007)
Kim, T., Adali, T.: Fully complex multi-layer perceptron network for nonlinear signal processing. Journal of VLSI Signal Processing 32(1/2), 29–43 (2002)
Omkar, S.N., Karanth, U.R.: Rule extraction for classification of acoustic emission signals using ant colony optimisation. Engineering Applications of Artificial Intelligence 21(8), 1381–1388 (2008)
Suresh, S., Omkar, S.N., Mani, V., Menaka, C.: Classification of acoustic emission signal using Genetic Programming. Journal of Aerospace Science and Technology 56(1), 26–41 (2004)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Suresh, S., Sundararajan, N., Savitha, R. (2013). Performance Study on Real-valued Classification Problems. In: Supervised Learning with Complex-valued Neural Networks. Studies in Computational Intelligence, vol 421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29491-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-29491-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29490-7
Online ISBN: 978-3-642-29491-4
eBook Packages: EngineeringEngineering (R0)