Park, Sang-Hyeun ; Fürnkranz, Johannes (2012)
Efficient prediction algorithms for binary decomposition techniques.
In: Data Mining and Knowledge Discovery, 24 (1)
doi: 10.1007/s10618-011-0219-9
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this article, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Park, Sang-Hyeun ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | Efficient prediction algorithms for binary decomposition techniques |
| Sprache: | Englisch |
| Publikationsjahr: | 2012 |
| Verlag: | Springer Netherlands |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Data Mining and Knowledge Discovery |
| Jahrgang/Volume einer Zeitschrift: | 24 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1007/s10618-011-0219-9 |
| Kurzbeschreibung (Abstract): | Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this article, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies. |
| Freie Schlagworte: | aggregation, binary decomposition, efficient decoding, efficient voting, multiclass classification, pairwise classification, ternary ECOC |
| Zusätzliche Informationen: | 10.1007/s10618-011-0219-9 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik > Knowledge Engineering 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 24 Jun 2011 13:31 |
| Letzte Änderung: | 05 Mär 2013 09:49 |
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