Abstract
Effective modeling based on the high dimensional data needs feature selection and fast learning speed. Aim at this problem, a novel modeling approach based on mutual information and extreme learning machines is proposed in this paper. Simple mutual information based feature selection method is integrated with the fast learning kernel based extreme learning machines to obtain better modeling performance. In the method, optimal number of the features and learning parameters of models are selected simultaneously. The simulation results based on the near-infrared spectrum show that the proposed approach has better prediction performance and fast leaning speed.
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References
Jiménez-Rodríguez, L.O., Arzuaga-Cruz, E., Vélez-Reyes, M.: Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data. IEEE Transaction on Geoscience and Remote Sensing 45, 469–483 (2007)
Wang, L.: Feature Selection with Kernel Class Separability. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1534–1546 (2008)
Leardi, R., Seasholtz, M.B., Pell, R.J.: Variable Selection for Multivariate Calibration Using a Genetic Algorithm: Prediction of Additive Concentrations in Polymer Films from Fourier Transform-infrared Spectral Data. Analytica Chimica Acta 461, 189–200 (2002)
Liu, H.W., Sun, J.G., Liu, L., Zhang, H.J.: Feature Selection with Dynamic Mutual Information. Pattern Recognition 42, 1330–1339 (2009)
Peng, H.C., Long, F.H., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-dependency, Max-relevance, and Min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1226–1238 (2005)
Tan, C., Li, M.L.: Mutual Information-induced Interval Selection Combined with Kernel Partial Least Squares for Near-infrared Spectral Calibration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 71, 1266–1273 (2008)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: Theory and Applications. Neurocomputing 70, 489–501 (2006)
Huang, G.B., Zhou, H.M., Ding, X.J., Zhang, R.: Extreme learning machines for regression and muticlass classification. IEEE Transaction on Systems Man and Cybernetics-part B: Cybernetics 42, 513–529 (2011)
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Transaction on Neural Network 5, 537–550 (1994)
Huang, G.B., Chen, L., Siew, C.K.: Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes. IEEE Transactions on Neural Networks 17, 879–892 (2006)
Huang, G.B., Chen, L.: Convex Incremental Extreme Learning Machine. Neurocomputing 70, 3056–3062 (2007)
Huang, G.B., Chen, L.: Enhanced Random Search Based Incremental Extreme Learning Machine. Neurocomputing 71, 3460–3468 (2008)
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Zhao, LJ., Tang, J., Chai, Ty. (2012). Modeling Spectral Data Based on Mutual Information and Kernel Extreme Learning Machines. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_4
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DOI: https://doi.org/10.1007/978-3-642-31346-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31345-5
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