Affiliation:
1. Department of Computer Science and Numerical Analysis, University of Córdoba, Spain
Abstract
Multilabel learning has become a relevant learning paradigm in the past years due to the increasing number of fields where it can be applied and also to the emerging number of techniques that are being developed. This article presents an up-to-date tutorial about multilabel learning that introduces the paradigm and describes the main contributions developed. Evaluation measures, fields of application, trending topics, and resources are also presented.
Funder
Ministry of Science and Technology project TIN-2011-22408
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference188 articles.
1. Pattern classification of dermoscopy images: A perceptually uniform model
2. Fabio Aiolli. 2004. Large Margin Multiclass Learning: Models and Algorithms. PhD Dissertation. Università degli Studi di Pisa. Fabio Aiolli. 2004. Large Margin Multiclass Learning: Models and Algorithms. PhD Dissertation. Università degli Studi di Pisa.
3. J. L. Ávila. 2013. Modelos de aprendizaje basados en programación genética para clasificación multietiqueta. PhD Dissertation. University of Córdoba. J. L. Ávila. 2013. Modelos de aprendizaje basados en programación genética para clasificación multietiqueta. PhD Dissertation. University of Córdoba.
4. Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study
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