Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach

doi:10.1016/j.amc.2013.12.087

Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach

2014-03-01

15

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AI

J

Javier Martínez Martínez *

C

Carla Iglesias Comesaña

DOI：10.1016/j.amc.2013.12.087

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Abstract

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We describe a new classification methodology based on binary classifiers constructed using support vector machines and applying a one-versus-all approach supported by the use of the directed acyclic graphs. The new methodology, which is computationally less costly because a smaller number of binary classification problems have to be resolved, was validated using UCI Machine Learning Repository data sets. Results point to the improved performance of the proposed model compared to approaches based on the one-versus-one and directed acyclic graph techniques. This new multiclassification strategy successfully applied to a slate tile classification problem produced favourable results compared to other validated techniques. (C) 2013 Elsevier Inc. All rights reserved.

Keywords:

Support vector machines

Directed acyclic graphs

One-versus-all

UCI Machine Learning Repository

Slate tile classification

Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach

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