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ESANN
2006

Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks

9 years 5 months ago
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks
Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals greater than that value are considered as errors. The algorithm was empirically evaluated and its results were analyzed also by means of REC curves.
Aloísio Carlos de Pina, Gerson Zaverucha
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Where ESANN
Authors Aloísio Carlos de Pina, Gerson Zaverucha
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