With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Multi-label classification (MLC) is a prediction task where each sample can have more than
one label. We propose a novel contrastive learning boosted multi-label prediction model …

Multi-label learning (MLL) usually requires assigning multiple relevant labels to each
instance. While a fully supervised MLL dataset needs a large amount of labeling effort, using …

Complementary label learning (CLL) requires annotators to give irrelevant labels instead of
relevant labels for instances. Currently, CLL has shown its promising performance on multi …

Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide
range of applications for visual tasks. Due to its complicated setting, prior arts have …

Multi-Label Classification (MLC), which aims to assign multiple labels to each sample
simultaneously, has achieved great success in a wide range of applications. MLC saves …

Multi-label classification is an arduous problem given the complication in label correlation.
Whilst sharing a common goal with contrastive learning in utilizing correlations for …

Missing values are a common problem in data science and machine learning. Removing
instances with missing values is a straightforward workaround, but this can significantly …

Recurrent Classifier Chains (RCCs) are a leading approach for multi-label classification as
they directly model the interdependencies between classes. Unfortunately, existing RCCs …

Multi-label classification (MLC) refers to the problem of tagging a given instance with a set of
relevant labels. Most existing MLC methods are based on the assumption that the …