Improving the accuracy of the optimum-path forest supervised classifier for large datasets
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Data
2010-12-15
Autores
Castelo-Fernández, César
De Rezende, Pedro J.
Falcão, Alexandre X.
Papa, João Paulo [UNESP]
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Resumo
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. © 2010 Springer-Verlag.
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Palavras-chave
Learning Algorithm, Optimum-Path Forest Classifier, Outlier Detection, Supervised Classification, Classification time, False negatives, False positive, Forest classifiers, Large datasets, New approaches, Supervised classification, Supervised classifiers, Supervised pattern recognition, Training sets, Training time, Classification (of information), Classifiers, Computer vision, Data mining, Learning algorithms
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6419 LNCS, p. 467-475.