Different from single-label learning, multi-label learning has rich semantic information. Label embedding obtains the inherent intelligence of the label space by projecting the label space …
In multi-label learning problems, the class labels are correlated and the label correlations can be leveraged to improve the predictive performance of a classifier. Methods that …
Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text …
S Bakhshi, F Can - Knowledge-Based Systems, 2024 - Elsevier
Available works addressing multi-label classification in a data stream environment focus on proposing accurate prediction models; however, they struggle to balance effectiveness and …
The problem of multi-label classification with missing labels (MLML) is a common challenge that is prevalent in several domains, eg image annotation and auto-tagging. In multi-label …
Y Wang, C Wang, T Deng, W Li - Information Sciences, 2024 - Elsevier
Feature selection is one of the important pre-processing methods for dimensionality reduction in multi-label learning tasks, which has attracted extensive attention in recent …
C Zhu, P Wang, L Ma, R Zhou, L Wei - Neural Computing and Applications, 2020 - Springer
Multi-view multi-label learning is widely used in multiple fields, and it aims to process data sets represented by multiple forms (views) and labeled by multiple classes. But most real …
X Wang, X Xiong, C Ning - IEEE Access, 2019 - ieeexplore.ieee.org
For remote sensing (RS) scene classification, most of the existing techniques annotate a scene image with merely a single semantic label. However, with the recent advance of …