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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 4/2017

17.05.2017 | Regular Paper

On the use of different base classifiers in multiclass problems

verfasst von: L. Morán-Fernández, V. Bolón-Canedo, A. Alonso-Betanzos

Erschienen in: Progress in Artificial Intelligence | Ausgabe 4/2017

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Abstract

Classification problems with more than two classes can be handled in different ways. The most used approach is the one which transforms the original multiclass problem into a series of binary subproblems which are solved individually. In this approach, should the same base classifier be used on all binary subproblems? Or should these subproblems be tuned independently? Trying to answer this question, in this paper we propose a method to select a different base classifier in each subproblem—following the one-versus-one strategy—making use of data complexity measures. The experimental results on 17 real-world datasets corroborate the adequacy of the method.
Vorheriger Artikel Incremental contingency planning for recovering from critical outcomes in high-probability seed plans
Nächster Artikel Applying named entity recognition and co-reference resolution for segmenting English texts

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Metadaten
Titel
On the use of different base classifiers in multiclass problems
verfasst von
L. Morán-Fernández
V. Bolón-Canedo
A. Alonso-Betanzos
Publikationsdatum
17.05.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 4/2017
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-017-0126-4

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