Cost-sensitive feature selection on multi-label data via neighborhood granularity and label enhancem...
Cost-sensitive feature selection on multi-label data via neighborhood granularity and label enhancement
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Publisher
New York: Springer US
Journal title
Language
English
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Publication information
Publisher
New York: Springer US
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Scope and Contents
Contents
Multi-label feature selection, which is an efficient and effective pre-processing step in machine learning and data mining, can select a feature subset that contains more contributions for multi-label classification while improving the performance of the classifiers. In real-world applications, an instance may be associated with multiple related la...
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Full title
Cost-sensitive feature selection on multi-label data via neighborhood granularity and label enhancement
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Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2509909106
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2509909106
Other Identifiers
ISSN
0924-669X
E-ISSN
1573-7497
DOI
10.1007/s10489-020-01993-w
