Improving the Accuracy of the Optimum-Path Forest Supervised Classifier for Large Datasets

  • César Castelo-Fernández
  • Pedro J. de Rezende
  • Alexandre X. Falcão
  • João Paulo Papa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

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.

Keywords

Optimum-Path Forest Classifier Outlier Detection Supervised Classification Learning Algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • César Castelo-Fernández
    • 1
  • Pedro J. de Rezende
    • 1
  • Alexandre X. Falcão
    • 1
  • João Paulo Papa
    • 2
  1. 1.Institute of ComputingState University of Campinas- UNICAMPCampinasBrazil
  2. 2.Department of ComputingSão Paulo State University-UNESPBaurúBrazil

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