Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …

Multi-label classification is an important yet challenging task in natural language processing.
It is more complex than single-label classification in that the labels tend to be correlated …

Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …

Predicting subcellular protein localization has become a popular topic due to its utility in
understanding disease mechanisms and developing innovative drugs. With the rapid …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

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 classification (MLC) has recently attracted increasing interest in the machine
learning community. Several studies provide surveys of methods and datasets for MLC, and …