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 …

Existing multi-label learning approaches mainly utilize an identical data representation
composed of all the features in the discrimination of all the labels, and assume that all the …

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

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 …

In the multi-view multi-label (MVML) classification problem, multiple views are
simultaneously associated with multiple semantic representations. Multi-view multi-label …

In multi-label learning, each example is represented by a single instance and associated
with multiple class labels. Existing multi-label learning algorithms mainly exploit label …

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 …

Multi-label classification remains a challenging task for high-dimensional data samples and
their labels both increase the complexity of training models. In this paper, we propose a …

In multi-view multi-label learning, each object is represented by multiple data views, and
belongs to multiple class labels simultaneously. Generally, all the data views have a …

Multi-Label Classification refers to the classification task where a data sample is associated
with multiple labels simultaneously, which is widely used in text classification, image …