A pragmatic approach to multi-class classification

Thomas Kopinski 1 Stéphane Magand 2 Uwe Handmann 1 Alexander Gepperth 3, 2, *
* Corresponding author
3 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
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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Conference papers
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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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