Feature augmentation, which manipulates the feature space by integrating the label
information, is one of the most popular strategies for solving Multi-Dimensional Classification …

Multi-dimensional classification (MDC) can be employed in a range of applications where
one needs to predict multiple class variables for each given instance. Many existing MDC …

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each
example, where class variables from different class spaces characterize semantics of the …

Multi-dimensional classification (MDC) refers to learning an association between individual
inputs and their multiple dimensional output discrete variables, and is thus more general …

Barking is perhaps the most characteristic form of vocalization in dogs; however, very little is
known about its role in the intraspecific communication of this species. Besides the obvious …

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models
recently proposed to deal with multi-dimensional classification problems, where each …

In traditional classification framework, the semantics of each object is usually characterized
by annotating a single class label from one homogeneous label space. Nonetheless, objects …

Computational systems that process multiple affective states may benefit from explicitly
considering the interaction between the states to enhance their recognition performance …

Multi-label text classification has become progressively more important in recent years,
where each document can be given multiple labels concurrently. Multi-label text …

Learning the structure of continuous-time Bayesian networks directly from data has
traditionally been performed using score-based structure learning algorithms. Only recently …