Abstract
In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a network pruning algorithm acting on MultiLayer Perceptron topology is the foundation of the feature selection strategy. Feature selection is implemented within the back-propagation learning process and based on a measure of saliency derived from bell functions positioned between input and hidden layers and adaptively varied in shape and position during learning. Performances were evaluated experimentally within a Remote Sensing study, aimed to classify hyperspectral data. A comparison analysis was conducted with Support Vector Machine and conventional statistical and neural techniques. As seen in the experimental context, the adaptive neural classifier showed a competitive behavior with respect to the other classifiers considered; it performed a selection of the most relevant features and showed a robust behavior operating under minimal training and noisy situations.
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References
Fukunaga, K., Hayes, R.R.: Effects of Sample Size Classifier Design. IEEE Trans. Pattern Analysis and Machine Intelligence 11(8), 873–885 (1989)
Jain, A.K., Duin, R.P., Mao, J.: Statistical Pattern Recognition: a review. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Jain, A., Zongker, D.: Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(2), 153–158 (1997)
Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, MA (1989)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Reed, R.: Pruning Algorithms - a survey. IEEE Trans. Neural Networks 5, 740–747 (1993)
Rumelhart, H., Hinton, G.E., Williams, R.J.: Learning Internal Representation by Error Propagation. In: Rumelhart, H., Mc Lelland, J.L. (eds.) Parallel Distributed Processing, pp. 318–362. MIT Press, Cambridge (1986)
Van Genderen, J.L., Lock, B.F., Vass, P.A.: Remote Sensing: Statistical testing of thematic map accuracy. In: Remote Sensing of Environment, vol. 7, pp. 3–14 (1978)
Kruse, F.A., Lefkoff, A.B., Boardman, J.B., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., Goetz, A.F.H.: The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer Data. In: Remote Sensing of Environment, vol. 44, pp. 145–163 (1993)
ENVI, The Environment for Visualizing Images. Research Systems Inc., http://www.rsinc.com/envi
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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© 2005 Springer-Verlag Berlin Heidelberg
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Binaghi, E., Gallo, I., Boschetti, M., Brivio, P.A. (2005). A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_92
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DOI: https://doi.org/10.1007/11553595_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
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