Abstract
Data discretization is an important processing step for several computational methods that work only with binary input data. In this work a method for discretize continuous data based on the use of the Extreme Learning Machine neural network architecture is developed and tested. The new method does not use data labels for performing the discretization process and thus is suitable for supervised and supervised data and also, as it is based on the Extreme Learning Machine, is very fast even for large input data sets. The efficiency of the new method is analyzed on several benchmark functions, testing the classification accuracy obtained with raw and discretized data, and also in comparison to results from the application of a state-of-the-art supervised discretization algorithm. The results indicate the suitability of the developed approach.
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References
Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A., Mayoraz, E., Muchnik, I.B.: An Implementation of Logical Analysis of Data. IEEE Trans. Knowl. Data Eng. 12, 292–306 (2000)
Franco, L., Anthony, M.: The Influence of Oppositely Classified Examples on the Generalization Complexity of boolean functions. IEEE Trans. Neural Netw. 17(3), 578–590 (2006)
Gómez, I., Franco, L., Jerez, J.M.: Neural Network Architecture Selection: Can Function Complexity Help? Neural Proc. Lett. 30, 71–87 (2009)
Huang, G.B., Zhu, Q.Y., Mao, K.Z., Siew, C.K., Saratch, P., Sundararajan, N.: Can Threshold Networks be Trained Directly. IEEE Trans. Circuits Syst. II. 53, 187–191 (2006)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. In: Proc. Int. Joint Conf. Neural Networks, pp. 985–990 (2004)
Kurgan, L.A., Cios, K.J.: Caim Discretization Algorithm. IEEE Trans. on Knowl. and Data Eng. 16(2), 145–153 (2004)
Subirats, J.L., Franco, L., Jerez, J.M.: C-mantec: a Novel Constructive Neural Network Algorithm Incorporating Competition between Neurons. Neural Networks 26, 130–140 (2012)
Subirats, J.L., Jerez, J.M., Franco, L.: A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions. IEEE Trans. on Circuits and Systems 55-I(10), 3188–3196 (2008)
Urda, D., Cañete, E., Subirats, J.L., Franco, L., Llopis, L., Jerez, J.M.: Energy Efficient Reprogramming in wsn Using Constructive Neural Networks. International Journal of Innovative Computing, Information and Control 8 (2012)
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© 2012 Springer-Verlag Berlin Heidelberg
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Carneros, J.J., Jerez, J.M., Gómez, I., Franco, L. (2012). Data Discretization Using the Extreme Learning Machine Neural Network. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_35
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DOI: https://doi.org/10.1007/978-3-642-34478-7_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
Online ISBN: 978-3-642-34478-7
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