In industrial Internet of Things applications with sensors sending dynamic process data at
high speed, producing actionable insights at the right time is challenging. A key problem …

Multi-dimensional classification is a cutting-edge problem, in which the values of multiple
class variables have to be simultaneously assigned to a given example. It is an extension of …

Multi-dimensional classification (MDC) aims at learning from objects where each of them is
represented by a single instance while associated with multiple class variables. In recent …

Drifting data streams and multi-label data are both challenging problems. Multi-label
instances may simultaneously be associated with many labels and classifiers must predict …

The development in the fields of clean energy, particularly wind and photovoltaic power,
generates a large amount of data streams, and how to mine valuable information from these …

In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is
a generalization of the multi-trait regressor stacking method. The proposed BMORS model …

Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted
topological structure, which are tailored to classifying data instances into multiple …

In bioacoustic recognition approaches, a “flat” classifier is usually trained to recognize
several species of anurans, where the number of classes is equal to the number of species …

In bioacoustic recognition approaches, a “flat” classifier is usually trained to recognize
several species of anuran, where the number of classes is equal to the number of species …

Real-world data stream classification often deals with multiple types of concept drift,
categorized by change characteristics such as speed, distribution, and severity. When labels …