Actas de congresos
Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets
Fecha
2011-01-01Registro en:
Multiple Classifier Systems. Berlin: Springer-verlag Berlin, v. 6713, p. 237-+, 2011.
0302-9743
WOS:000309192000026
Autor
Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
Institución
Resumen
The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OFF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure, The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OFF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more.