Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

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 …

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 …

Label ambiguity and data complexity are widely recognized as major challenges in multi-
label classification. Existing studies strive to find approximate representations concerning …

Multi-label classification is a challenging issue in the data science community due to the
ambiguity of label semantics. Existing studies mainly focus on improving label association …

Multi-label classification is a challenging issue because it simultaneously embraces the
characteristics of the imbalanced class distribution for each label and the uncertain label …

Multi-label data classification (MLC) has become an increasingly active research area over
the past decade. MLC refers to a classification problem where each instance can be …

Multi-label data is prevalent across various applications, where instances can be annotated
with a set of classes. Although multi-label data can take various forms, such as images and …

Data representation is of significant importance in minimizing multi-label ambiguity. While
most researchers intensively investigate label correlation, the research on enhancing model …

Ensemble approach for multi-label classification has received great attention from the
Machine Learning community over the last decade. It has been developed on top of the …