Improving multiclass pattern recognition by the combination of two strategies
We present a new method of multiclass classification based on the combination of one- vs- all method and a modification of one- vs- one method. This combination of one- vs- all and one- vs- one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided.
multiclass classification one-vs-one one-vs-all neural networks support vector machines