Robust Multi-Label Classification with Enhanced Global and Local Label Correlation

Robust Multi-Label Classification with Enhanced Global and Local Label Correlation

Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main reasons that model robustness degrades. Aiming at the...

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Bibliographic Details
Main Authors: Tianna Zhao, Yuanjian Zhang, Witold Pedrycz
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Mathematics
Subjects:
multi-label classification
label correlations
noisy features
missing labels
robustness
Online Access:https://www.mdpi.com/2227-7390/10/11/1871

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