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Granular Regression

  • Conference paper
Neural Nets (WIRN 2005, NAIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3931))

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Abstract

We augment a linear regression procedure by a thruth-functional method in order to identify a highly informative regression line. The idea is to use statistical methods to identify a confidence region for the line and exploit the structure of the sample data falling in this region for identifying the most fitting line. The fitness function is related to the fuzziness of the sampled points as a natural extension of the statistical criterion ruling the identification of the confidence region within the Algorithmic Inference approach. We tested the method on three well known benchmarks.

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© 2006 Springer-Verlag Berlin Heidelberg

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Apolloni, B., Iannizzi, D., Malchiodi, D., Pedrycz, W. (2006). Granular Regression. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_22

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  • DOI: https://doi.org/10.1007/11731177_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33183-4

  • Online ISBN: 978-3-540-33184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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