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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3290797
|2 doi
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Liu, Bing-Qing
|e verfasserin
|4 aut
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| 245 |
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|a Towards Enabling Binary Decomposition for Partial Multi-Label Learning
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 03.10.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Partial multi-label learning (PML) is an emerging weakly supervised learning framework, where each training example is associated with multiple candidate labels which are only partially valid. To learn the multi-label predictive model from PML training examples, most existing approaches work by identifying valid labels within candidate label set via label confidence estimation. In this paper, a novel strategy towards partial multi-label learning is proposed by enabling binary decomposition for handling PML training examples. Specifically, the widely used error-correcting output codes (ECOC) techniques are adapted to transform the PML learning problem into a number of binary learning problems, which refrains from using the error-prone procedure of estimating labeling confidence of individual candidate label. In the encoding phase, a ternary encoding scheme is utilized to balance the definiteness and adequacy of the derived binary training set. In the decoding phase, a loss weighted scheme is applied to consider the empirical performance and predictive margin of derived binary classifiers. Extensive comparative studies against state-of-the-art PML learning approaches clearly show the performance advantage of the proposed binary decomposition strategy for partial multi-label learning
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|a Journal Article
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|a Jia, Bin-Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Min-Ling
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 11 vom: 12. Nov., Seite 13203-13217
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:45
|g year:2023
|g number:11
|g day:12
|g month:11
|g pages:13203-13217
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|u http://dx.doi.org/10.1109/TPAMI.2023.3290797
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