Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

Conservationists are increasingly using autonomous acoustic recorders to determine the
presence/absence and the abundance of bird species. Unlike humans, these recorders can …

The great attention given by the scientific community to multi-label learning in recent years
has led to the development of a large number of methods, many of them based on …

Using multimedia identification tools is considered as one of the most promising solutions to
help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic …

Multi-label learning has become an important area of research due to the increasing number
of modern applications that contain multi-label data. The multi-label data are structured in a …

Multi-label learning on real-world data is a challenging task due to sparse labels, missing
labels, and sparse structures. Some existing approaches are effective in addressing the …

Abstract Nowadays, Multi-Label Feature Selection (MLFS) attracts more and more attention
to tackle the high-dimensional problem in multi-label data. A key characteristic of existing …

Multi-label classification has gained in importance in the last decade and it is today
confronted to the current needs to process massive raw data from heterogeneous sources …

Data labelling is commonly an expensive process that requires expert handling. In multi-
label data, data labelling is further complicated owing to the experts must label several times …

In many real-world applications, an instance from the training dataset of multi-label learning
(MLL) often has some irrelevant labels. Traditional MLL and partial label learning (PLL) …