The family of methods collectively known as classifier chains has become a popular
approach to multi-label learning problems. This approach involves chaining together off-the …

Nowadays, many real-world applications of our daily life generate massive volume of
streaming data at a higher speed than ever before, to name a few, Web clicking data …

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

Multi-label classification is the task of predicting a set of labels for a given input instance.
Classifier chains are a state-of-the-art method for tackling such problems, which essentially …

In many practical applications of supervised learning the task involves the prediction of
multiple target variables from a common set of input variables. When the prediction targets …

Binary Relevance is a well-known framework for multi-label classification, which considers
each class label as a binary classification problem. Many existing multi-label algorithms are …

One of the basic measures of the World Health Organization (WHO) in the fight against the
COVID-19 pandemic is a lockdown policy with reduced contacts and physical distance. This …

Multilabel learning deals with examples having multiple class labels simultaneously. It has
been applied to a variety of applications, such as text categorization and image annotation …

Binary relevance (BR) is a well-known framework for multi-label classification. It
decomposes multi-label classification into binary (one-vs-rest) classification subproblems …

Differential evolution is one of the most powerful stochastic real-parameter optimization
algorithms currently, and its performance depends heavily on control parameters and …