Multilabel classification as a data mining task has recently attracted increasing interest from
researchers. Many current data mining applications address problems with instances that …

scikit-multilearn is a Python library for performing multi-label classification. The library is
compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal …

In recent years, many different differential evolution (DE) variants have been proposed to
solve real-world optimization problems. However, the performance of them is largely …

The random k-labelsets ensemble (RAkEL) is a multi-label learning strategy that integrates
many single-label learning models. Each single-label model is constructed using a label …

Background: Metabolic chemical reaction is one of the main types of fundamental processes
to maintain life. Generally, each reaction needs an enzyme. The metabolic pathway collects …

Outcome-based education (OBE) is a well-proven teaching strategy based upon a
predefined set of expected outcomes. The components of OBE are Program Educational …

Earth observation from satellite sensory data poses challenging problems, where machine
learning is currently a key player. In recent years, Gaussian Process (GP) regression has …

Multi-dimensional classification (MDC) is the supervised learning problem where an
instance is associated with multiple classes, rather than with a single class, as in traditional …

First proposed in 2009, the classifier chains model (CC) has become one of the most
influential algorithms for multi-label classification. It is distinguished by its simple and …

Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary
classifiers according to a label order. Each classifier in the sequence is responsible for …