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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Efficient supervised optimum-path forest classification for large datasets

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Author(s):
Papa, Joao P. [1] ; Falcao, Alexandre X. [2] ; de Albuquerque, Victor Hugo C. [3] ; Tavares, Joao Manuel R. S. [4]
Total Authors: 4
Affiliation:
[1] UNESP Univ Estadual Paulista, Dept Comp, Bauru - Brazil
[2] Univ Estadual Campinas, Inst Comp, Campinas - Brazil
[3] Univ Fortaleza, Ctr Ciencias Tecnol, Fortaleza, Ceara - Brazil
[4] Univ Porto, Fac Egenharia, P-4100 Oporto - Portugal
Total Affiliations: 4
Document type: Journal article
Source: PATTERN RECOGNITION; v. 45, n. 1, p. 512-520, JAN 2012.
Web of Science Citations: 132
Abstract

Today data acquisition technologies come up with large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient without losing their effectiveness. We have tried to circumvent the problem by reducing it into the fast computation of an optimum-path forest (OPF) in a graph derived from the training samples. In this forest, each class may be represented by multiple trees rooted at some representative samples. The forest is a classifier that assigns to a new sample the label of its most strongly connected root. The methodology has been successfully used with different graph topologies and learning techniques. In this work, we have focused on one of the supervised approaches, which has offered considerable advantages over Support Vector Machines and Artificial Neural Networks to handle large datasets. We propose (i) a new algorithm that speeds up classification and (ii) a solution to reduce the training set size with negligible effects on the accuracy of classification, therefore further increasing its efficiency. Experimental results show the improvements with respect to our previous approach and advantages over other existing methods, which make the new method a valuable contribution for large dataset analysis. (C) 2011 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 07/52015-0 - Approximation methods for visual computing
Grantee:Jorge Stolfi
Support type: Research Projects - Thematic Grants