A Review on Multi-Label Learning

doi:10.3969/j.issn.1006-2475.2015.08.010

Abstract

Abstract: Multi-label learning is different from the traditional supervised learning. It is a framework which is proposed to represent objects which might have multiple semantic meanings simultaneously in the external world. Under this framework, an instance might be associated with a set of labels. Over the past decades, a lot of research results of multi-label learning have been achieved and gotten extensive application. This paper provides a systematic and detailed review in this area. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly, some important and classic multi-label learning algorithms are presented. Finally, some valuable research directions in this area are discussed.
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Key words: multi-label learning, evaluation metrics, problem transformation, algorithm adaptation, machine learning, data mining

CLC Number:

TP181

LIANG Wei-chao， SONG Bin. A Review on Multi-Label Learning[J]. Computer and Modernization, 2015, 0(8): 48-56.

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