Multi-label feature selection is an efficient technique to deal with the high dimensional multi-
label data by selecting the optimal feature subset. Existing researches have demonstrated …

The adoption of machine learning in materials science has rapidly transformed materials
property prediction. Hurdles limiting full capitalization of recent advancements in machine …

Effective solutions to conserve biodiversity require accurate community‐and species‐level
information at relevant, actionable scales and across entire species' distributions. However …

Multi-label data classification has received much attention due to its wide range of
application domains. Unfortunately, a class imbalance problem often occurs in multi-label …

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 …

A well-known challenge associated with the multi-label classification problem is modelling
dependencies between labels. Most attempts at modelling label dependencies focus on co …

Multi-label feature selection (MFS) has gained in importance, and it is today confronted with
the current need to process multi-semantic high-dimensional data. Recent studies usually …

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 …

Exploiting correlations among class variables and using them to facilitate the learning
process are a key challenge of Multi-Dimensional Classification (MDC) problems. Label …

Generalized zero-shot extreme multi-label learning (GZXML) aims to predict relevant labels
for unknown instances from a set of seen and unseen labels and is widely used in …