Most of the existing methods of multi-class classification are either based on binary classifiers or are reduced to them. The general idea of such an approach is to use a set of binary classifiers trained to separate different groups of objects from each other. With such a multi-class classification, various voting schemes for a set of binary classifiers are used.
In the one-against-all strategy for N classes, N classifiers are trained, each of which separates its class from all other classes. At the recognition stage, the unknown vector X is fed to all N classifiers. The membership of the vector X is determined by the classifier that gave the highest estimate. This approach can meet the problem of class imbalances ...
