Efficient supervised optimum-path forest classification for large datasets

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Papa, JP (Author) Other; Falcao, AX (Author) Other; de Albuquerque, VHC (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Pattern Recognition

Vol. 45 No. 1

Pages: 512-520

ISSN: 0031-3203

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 135 Citations

Scopus - 190 Citations

Other information

Authenticus ID: P-002-HKN

DOI: 10.1016/j.patcog.2011.07.013

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9