Publication: Improving the Accuracy of the Optimum-Path Forest Supervised Classifier for Large Datasets
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Date
2010-01-01
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Type
Work presented at event
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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 OFF while still gaining in classification time, at the expense of a slight increase in training time.
Description
Language
English
Citation
Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 6419, p. 467-+, 2010.
