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PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
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2024 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 661, article id 120165Article in journal (Refereed) Published
Abstract [en]

Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the Encoder-Decoder Framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal Encoder-Decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria.

Place, publisher, year, edition, pages
2024. Vol. 661, article id 120165
Keywords [en]
Partial multi-label learning, Label correlation, Label disambiguation, Encoder-Decoder framework, Conditional layer normalization
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:du-47973DOI: 10.1016/j.ins.2024.120165Scopus ID: 2-s2.0-85183583505OAI: oai:DiVA.org:du-47973DiVA, id: diva2:1834644
Available from: 2024-02-05 Created: 2024-02-05 Last updated: 2024-02-05

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Han, Mengjie

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf