As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

Multi-label learning deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

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 …

As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …

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 …