Designing multi-label classifiers that maximize F measures: State of the art

标题
Designing multi-label classifiers that maximize F measures: State of the art
作者
关键词
Multi-label classification, F, measure, Learning algorithms, Empirical utility maximization, Decision-theoretic approach
出版物
PATTERN RECOGNITION
Volume 61, Issue -, Pages 394-404
出版商
Elsevier BV
发表日期
2016-08-15
DOI
10.1016/j.patcog.2016.08.008

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