Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

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 …

Multi-label learning has received significant attention in the research community over the
past few years: this has resulted in the development of a variety of multi-label learning …

A simple yet effective multilabel learning method, called label powerset (LP), considers each
distinct combination of labels that exist in the training set as a different class value of a single …

A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …

Multi-label learning originated from the investigation of text categorization problem, where
each document may belong to several predefined topics simultaneously. In multi-label …

Multi-label learning deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

In multilabel learning, each instance in the training set is associated with a set of labels and
the task is to output a label set whose size is unknown a priori for each unseen instance. In …

Multi-label Learning is a form of supervised learning where the classification algorithm is
required to learn from a set of instances, each instance can belong to multiple classes and …

A reliable forest fire susceptibility map is a necessity for disaster management and a primary
reference source in land use planning. We set out to evaluate the use of the LogitBoost …