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.
García-Pedrajas
Nicolás
N
Department of Computing and Numerical Analysis, Edificio Einstein, 3a Planta, Campus Universitario de Rabanales, 14071 Córdoba, Spain. npedrajas@uco.es
Ortiz-Boyer
Domingo
D
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*