Journal ArticleDOI
Multi-label learning: a review of the state of the art and ongoing research
TLDR
The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.Abstract:
Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up-to-date overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented. WIREs Data Mining Knowl Discov 2014, 4:411-444. doi: 10.1002/widm.1139read more
Citations
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Measuring statistical dependence with Hilbert-Schmidt norms
TL;DR: An independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator, or HSIC, is proposed.
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification.
Weiwei Cheng,Eyke Hüllermeier +1 more
TL;DR: This paper proposes a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases, and allows one to capture interdependencies between labels and to combine model-based and similarity-based inference for multILabel classification.
Journal ArticleDOI
Multi-label learning with label-specific feature reduction
TL;DR: Experimental results show that the proposed multi-label learning approaches can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi- label learning approaches.
Journal ArticleDOI
Streaming Feature Selection for Multilabel Learning Based on Fuzzy Mutual Information
TL;DR: This paper introduces fuzzy mutual information to evaluate the quality of features in multilabel learning, and design efficient algorithms to conduct multILabel feature selection when the feature space is completely known or partially known in advance.
Journal ArticleDOI
Review of ensembles of multi-label classifiers: Models, experimental study and prospects
Jose M. Moyano,Eva Gibaja,Krzysztof J. Cios,Krzysztof J. Cios,Sebastián Ventura,Sebastián Ventura +5 more
TL;DR: A comparison of the state-of-the-art in ensembles of multi-label classifiers over a wide set of 20 datasets is carried out, evaluating their performance based on the characteristics of the datasets such as imbalance, dependence among labels and dimensionality.
References
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