Improving the accuracy of the optimum-path forest supervised classifier for large datasets

Authors:
César Castelo-Fernández;Pedro J. De Rezende;Alexandre X. Falcão;João Paulo Papa
Affiliations:
Institute of Computing, State University of Campinas, Campinas, Brazil;Institute of Computing, State University of Campinas, Campinas, Brazil;Institute of Computing, State University of Campinas, Campinas, Brazil;Department of Computing, São Paulo State University, Baurú, Brazil
Venue:
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Year:
2010

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Abstract

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time.