Reliable Multilabel Classification: Prediction with Partial Abstention
2020 ◽
Vol 34
(04)
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pp. 5264-5271
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Keyword(s):
In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.
2012 ◽
Vol 38
(3)
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pp. 222-233
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Keyword(s):
2020 ◽
Vol 34
(04)
◽
pp. 4158-4165
Keyword(s):
2021 ◽
Vol 9
(10)
◽
pp. 1341-1347
Keyword(s):
2019 ◽
Vol 6
(2)
◽
pp. 61-72
2020 ◽
Vol 15
(2)
◽
pp. 19-33
2009 ◽
Vol 33
(2)
◽
pp. 67-76
2011 ◽
pp. 1164-1169
Keyword(s):