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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

5. Adaptation-Based Classifiers

verfasst von : Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus

Erschienen in: Multilabel Classification

Verlag: Springer International Publishing

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Abstract

While data transformation is a relatively straightforward way to do multilabel classification through traditional classifiers, an alternative approach based on adapting those classifiers to tackle the original multilabeled data also has been also explored. This chapter aims to introduce many of these method adaptations. Most of them rely on traditional algorithms based on the trees, neural networks, instance-based learning, etc. A general overview of them is provided in Sect. 5.1. Then, about thirty different proposals are detailed in Sects. 5.2–5.7, grouped according to the type of model they are founded on. A selection of four algorithms are experimentally tested in Sect. 5.8. Some final remarks are provided in Sect. 5.9.

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Vorheriges Kapitel Transformation-Based Classifiers
Nächstes Kapitel Ensemble-Based Classifiers
Fußnoten
1
ML-TREE code can be downloaded from Dr. Qingyao Wu’s Web page at https://​sites.​google.​com/​site/​qysite. Rank-SVM code can be downloaded from http://​cse.​seu.​edu.​cn/​people/​zhangml/​files/​RankSVM.​rar.
 
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Metadaten
Titel
Adaptation-Based Classifiers
verfasst von
Francisco Herrera
Francisco Charte
Antonio J. Rivera
María J. del Jesus
Copyright-Jahr
2016
Buch
Multilabel Classification

Print ISBN: 978-3-319-41110-1

Electronic ISBN: 978-3-319-41111-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-41111-8_5

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