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WIREs Data Mining Knowl Discov
Impact Factor: 1.939

Multilabel feature selection: A comprehensive review and guiding experiments

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240

This article is categorized under:

  • Technologies > Data Preprocessing
Multilabel data
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Diagram of Nemenyi's post‐hoc test in terms of (a) accuracy and (b) hamming loss criteria. Methods which are not significantly different based on the critical difference (CD) (at α = .05) are connected
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Multilabel feature selection using binary relevance transformation method in internal form
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Multilabel feature selection using binary relevance transformation method in external form
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Multilabel feature selection using transformation strategy
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Categorization of multilabel feature selection methods
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Taxonomy of multilabel classification methods
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