Abstract
A well established technique to improve the classification performances is to combine more classifiers. In the binary case, an effective instrument to analyze the dichotomizers under different class and cost distributions providing a description of their performances at different operating points is the Receiver Operating Characteristic (ROC) curve. To generate a ROC curve, the outputs of the dichotomizers have to be processed. An alternative way that makes this analysis more tractable with mathematical tools is to use a parametric model and, in particular, the binormal model that gives a good approximation to many empirical ROC curves. Starting from this model, we propose a method to estimate the ROC curve of the linear combination of two dichotomizers given the ROC curves of the single classifiers. A possible application of this approach has been successfully tested on real data set.
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© 2005 Springer-Verlag Berlin Heidelberg
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Marrocco, C., Molinara, M., Tortorella, F. (2005). Estimating the ROC Curve of Linearly Combined Dichotomizers. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_95
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DOI: https://doi.org/10.1007/11553595_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
Online ISBN: 978-3-540-31866-8
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Support Vector Machine
- True Positive Rate
- Multi Layer Perceptron
- Cumulative Distribution Function
- Pima Indian Diabetes
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