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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15808
Title: A new parallel training algorithm for optimum-path forest-based learning
Authors: Culquicondor, Aldo
Castelo Fernández, Cesar
Paulo Papa, Joao
Keywords: Economic and social effects;Forestry;Parallel algorithms;Graph algorithms;Optimum-path forests;Parallel training;Parallelizing;Speed up;Trade off;Training phase;Pattern recognition
Issue Date: 2017
Publisher: Springer Verlag
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013418925&doi=10.1007%2f978-3-319-52277-7_24&partnerID=40&md5=b9c9313fb37394b9f9e08b705310a884
Abstract: In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness. © Springer International Publishing AG 2017.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15808
ISBN: 9783319522760
ISSN: 3029743
Appears in Collections:Artículos de investigación

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