Optimizing linear discriminant error correcting output codes using particle swarm optimization
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
| Hi-index | 0.00 |
Error-Correcting Output Codes (ECOC) with sub-classes reveal a common way to solve multi-class classification problems. According to this approach, a multi-class problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic mutual information (FQMI) procedure. However, FQMI is not applicable on large datasets due to its high algorithmic complexity. In this paper we propose Fisher's Linear Discriminant Ratio (FLDR) as an alternative decomposition criterion which is of much less computational complexity and achieves in most experiments conducted better classification performance. Furthermore, we compare FLDR against FQMI for facial expression recognition over the Cohn-Kanade database.