The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Many problem settings in machine learning are concerned with the simultaneous prediction
of multiple target variables of diverse type. Amongst others, such problem settings arise in …

Multi-label learning has attracted significant attention from both academic and industry field
in recent decades. Although existing multi-label learning algorithms achieved good …

Recognizing malware before its installation plays a crucial role in keeping an android device
safe. In this paper we describe a supervised method that is able to analyse multiple …

Interaction data are characterized by two sets of objects, each described by their own set of
features. They are often modeled as networks and the values of interest are the possible …

We present a unified framework called deep dependency networks (DDNs) that combines
dependency networks and deep learning architectures for multi-label classification, with a …

Dependencies between the labels are commonly regarded as the crucial issue in multi-label
classification. Rules provide a natural way for symbolically describing such relationships …

In multi-target prediction, an instance has to be classified along multiple target variables at
the same time, where each target represents a category or numerical value. There are …

Multilabel data comprise instances associated with multiple binary target variables. The
main learning task from such data is multilabel classification, where the goal is to output a …

Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor
is their proximity to customers. Their popularity has stimulated the expansion of available …