Hierarchical linear support vector machine

Autor: Carlos Santa Cruz, Irene Rodriguez-Lujan, Ramon Huerta
Přispěvatelé: UAM. Departamento de Ingeniería Informática
Rok vydání: 2012
Předmět:
Zdroj: Biblos-e Archivo. Repositorio Institucional de la UAM
instname
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2012.06.002
Popis: This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, Vol. 45, Iss. 12, (2012) DOI: 10.1016/j.patcog.2012.06.002
The increasing size and dimensionality of real-world datasets make it necessary to design efficient algorithms not only in the training process but also in the prediction phase. In applications such as credit card fraud detection, the classifier needs to predict an event in 10 ms at most. In these environments the speed of the prediction constraints heavily outweighs the training costs. We propose a new classification method, called a Hierarchical Linear Support Vector Machine (H-LSVM), based on the construction of an oblique decision tree in which the node split is obtained as a Linear Support Vector Machine. Although other methods have been proposed to break the data space down in subregions to speed up Support Vector Machines, the H-LSVM algorithm represents a very simple and efficient model in training but mainly in prediction for large-scale datasets. Only a few hyperplanes need to be evaluated in the prediction step, no kernel computation is required and the tree structure makes parallelization possible. In experiments with medium and large datasets, the H-LSVM reduces the prediction cost considerably while achieving classification results closer to the non-linear SVM than that of the linear case.
The authors would like to thank the anonymous reviewers for their comments that help improve the manuscript. I.R.-L. is supported by an FPU Grant from Universidad Autónoma de Madrid, and partially supported by the Universidad Autónoma de Madrid-IIC Chair and TIN2010-21575-C02-01. R.H. acknowledges partial support by ONRN00014-07-1-0741, USARIEM-W81XWH-10-C-0040 (ELINTRIX) and JPL-2012-1455933.
Databáze: OpenAIRE
Externí odkaz: