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

Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

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

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 …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

Multi-label classification has attracted increasing attention in various applications, such as
medical diagnosis and semantic annotation. With such trend, a large number of ensemble …

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 …

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 …

In multi-label learning, label correlations commonly exist in the data. Such correlation not
only provides useful information, but also imposes significant challenges for multi-label …