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Erschienen in:
New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Combining One-Versus-One and One-Versus-All Strategies to Improve Multiclass SVM Classifier

verfasst von : Wiesław Chmielnicki, Katarzyna Sta̧por

Erschienen in: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Verlag: Springer International Publishing

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Abstract

Support Vector Machine (SVM) is a binary classifier, but most of the problems we find in the real-life applications are multiclass. There are many methods of decomposition such a task into the set of smaller classification problems involving two classes only. Two of the widely known are one-versus-one and one-versus-rest strategies. There are several papers dealing with these methods, improving and comparing them. In this paper, we try to combine theses strategies to exploit their strong aspects to achieve better performance. As the performance we understand both recognition ratio and the speed of the proposed algorithm. We used SVM classifier on several different databases to test our solution. The results show that we obtain better recognition ratio on all tested databases. Moreover, the proposed method turns out to be much more efficient than the original one-versus-one strategy.

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Vorheriges Kapitel Combining One-vs-One Decomposition and Ensemble Learning for Multi-class Imbalanced Data
Nächstes Kapitel A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees
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Metadaten
Titel
Combining One-Versus-One and One-Versus-All Strategies to Improve Multiclass SVM Classifier
verfasst von
Wiesław Chmielnicki
Katarzyna Sta̧por
Copyright-Jahr
2016
Buch
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Print ISBN: 978-3-319-26225-3

Electronic ISBN: 978-3-319-26227-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-26227-7_4