New One (ProQuest: Formulae and/or non-USASCII text omitted)method: NOV

Binarization strategies decompose the original multi-class dataset into multiple two-class subsets, learning a different binary model for each new subset. One-vs-All (OVA) and One-vs-One (OVO) are two of the most well-known techniques: One-vs-One separates a pair of classes in each binary sub-proble...

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Bibliographic Details
Published in:Expert systems with applications 2014-10, Vol.41 (14), p.6251-6260
Main Authors: Arruti, A, Mendialdua, I, Sierra, B, Lazkano, E, Jauregi, E
Format: Article
Language:English
Subjects:
Classifiers
Decomposition
Empirical analysis
Expert systems
Learning
Proposals
Strategy
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Summary:Binarization strategies decompose the original multi-class dataset into multiple two-class subsets, learning a different binary model for each new subset. One-vs-All (OVA) and One-vs-One (OVO) are two of the most well-known techniques: One-vs-One separates a pair of classes in each binary sub-problem, ignoring the remaining ones: and One-vs-All distinguishes one class from all the other classes. In this paper, we present two new OVA and OVO combinations where the best base classifier is applied in each sub-problem. The first method is called OVA + OVO since it combines the outputs obtained by OVA and OVO decomposition strategies. The second combination is named New One (NOV, and its objective is to solve the problems found in OVA when different base classifiers are used in each sub-problem. In order to validate the performance of the new proposal, an empirical study has been carried out where the two new methods are compared with other well-known decomposition strategies from the literature. Experimental results show that both methods obtain promising results, especially NOV
ISSN:0957-4174
DOI:10.1016/j.eswa.2014.04.010