Abstract
Artificial neural networks (ANN) have been used as predictive systems for a variety of application domains such as science, engineering and finance. Therefore it is very important to be able to estimate the reliability of a given model. Bootstrap is a computer intensive method used for estimating the distribution of a statistical estimator based on an imitation of the probabilistic structure of the data generating process and the information contained in a given set of random observations. Bootstrap plans can be used for estimating the uncertainty associated with a value predicted by a feedforward neural network.
The available bootstrap methods for ANN assume independent random samples that are free of outliers. Unfortunately, the existence of outliers in a sample has serious effects such as some resamples may have a higher contamination level than the initial sample, and the model is affected because it is sensible to these deviations resulting on a poor performance.
In this paper we investigate a robust bootstrap method for ANN that is resistant to the presence of outliers and is computationally simple. We illustrate our technique on synthetic and real datasets and results are shown on confidence intervals for neural network prediction.
This work was supported in part by Research Grant Fondecyt 1010101 and 7010101, and in part by Research Grant DGIP-UTFSM.
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Allende, H., Ñanculef, R., Salas, R. (2004). Robust Bootstrapping Neural Networks. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_84
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DOI: https://doi.org/10.1007/978-3-540-24694-7_84
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