A simple technique for improving multi-class classification with neural networks
Abstract
We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.
Cite
@article{arxiv.1601.01157,
title = {A simple technique for improving multi-class classification with neural networks},
author = {Thomas Kopinski and Alexander Gepperth and Uwe Handmann},
journal= {arXiv preprint arXiv:1601.01157},
year = {2016}
}
Comments
European Symposium on artificial neural networks (ESANN), Jun 2015, Bruges, Belgium