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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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
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