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A pragmatic approach to multi-class classification

Abstract : We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.
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Contributor : Alexander Gepperth Connect in order to contact the contributor
Submitted on : Wednesday, January 6, 2016 - 10:18:16 AM
Last modification on : Saturday, June 25, 2022 - 9:09:52 PM
Long-term archiving on: : Thursday, April 7, 2016 - 3:53:32 PM


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Thomas Kopinski, Stéphane Magand, Uwe Handmann, Alexander Gepperth. A pragmatic approach to multi-class classification. European Symposium on artificial neural networks (ESANN), Apr 2015, Bruges, Belgium. ⟨10.1109/IJCNN.2015.7280768⟩. ⟨hal-01251382⟩



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