The family of methods collectively known as classifier chains has become a popular
approach to multi-label learning problems. This approach involves chaining together off-the …

Multi-label learning has been extensively studied in many areas such as information
retrieval, bioinformatics, and multimedia annotation. However, multi-label datasets often …

In traditional multi-label setting it is assumed that all relevant labels are assigned to the
given instance. In positive unlabelled setting, only some of relevant labels are assigned. The …

Feature selection is one of the trending challenges in multi-label classification. In recent
years a lot of methods have been proposed. However the existing approaches assume that …

We consider a multilabel all-relevant feature selection task which is more general than the
classical minimal-optimal subset task. Whereas the goal of the minimal-optimal methods is …

Text multi-label learning deals with examples having multiple labels simultaneously. It can
be applied to many fields, such as text categorization, medical diagnosis recognition and …

Large datasets computing is a research problem as well as a huge challenge due to
massive amounts of data that are mined and crunched in order to successfully analyze these …

Multi-label classification (MLC) is a supervised learning problem where an object is naturally
associated with multiple concepts because it can be described from various dimensions …

A large number and diversity of techniques have been offered in the literature in recent
years for solving multi-label classification tasks, including classifier chains where predictions …

Protein Function Prediction is a complex Hierarchical Multi-label Classification task where
the functional classes involved are organized in a hierarchy. While many Machine Learning …