Abstract
This paper presents a method to estimate the reliability of the output of a (possibly neuro-fuzzy) model by means of an additional neural network. The proposed technique is most effective when the reliability of the model significantly varies in different areas of input space, as it often happens in many real-world problems, allowing the user to predict how reliable is a given model for each specific situation. Alternatively, the proposed technique can be used to analyze particular anomalies of input data set such as the outliers.
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Reyneri, L.M., Colla, V., Sgarbi, M., Vannucci, M. (2009). Self-estimation of Data and Approximation Reliability through Neural Networks. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_12
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DOI: https://doi.org/10.1007/978-3-642-02478-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
Online ISBN: 978-3-642-02478-8
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