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Multi-Label Classification: An Overview
Abstract
Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
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“Even for a “simpler” output space such as the one present in the multi-label classification setting (Tsoumakas and Katakis 2006), it is not clear if the best option would be to explain each label individually or the set of predicted labels as a single label.”
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Metadata
- Authors
- Tsoumakas, G., & Katakis, I.
- Venue
- International Journal of Data Warehousing and Mining
- Publisher
- IGI Global
- Published
- 2007-07-01
- Volume
- 3
- Issue
- 3
- Pages
- 1-13
- ISSN
- 1548-3924 (ISSN-L)
- 1548-3932
- OpenAlex
- W2146241755
- Cited by
- 2,508
- References
- 2
- FWCI
- 55.55
- Type
- journal article
- Language
- ng
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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