Evidential calibration of binary SVM classifiers |
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Institution: | 1. Sorbonne universités, Université de technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, CS 60 319, 60 203 Compiègne cedex, France;2. Peking University, Key Laboratory of Machine Perception (MOE), Beijing, China |
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Abstract: | In machine learning problems, the availability of several classifiers trained on different data or features makes the combination of pattern classifiers of great interest. To combine distinct sources of information, it is necessary to represent the outputs of classifiers in a common space via a transformation called calibration. The most classical way is to use class membership probabilities. However, using a single probability measure may be insufficient to model the uncertainty induced by the calibration step, especially in the case of few training data. In this paper, we extend classical probabilistic calibration methods to the evidential framework. Experimental results from the calibration of SVM classifiers show the interest of using belief functions in classification problems. |
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Keywords: | Classifier calibration Theory of belief functions Dempster–Shafer theory Evidence theory Support vector machine |
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