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

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

Many of the existing machine learning algorithms, both supervised and unsupervised,
depend on the quality of the input characteristics to generate a good model. The amount of …

Synthesis prediction is a key accelerator for the rapid design of advanced materials.
However, determining synthesis variables such as the choice of precursor materials is …

Evolutionary fuzzy systems are one of the greatest advances within the area of
computational intelligence. They consist of evolutionary algorithms applied to the design of …

In multi-label classification, many existing works only pay attention to the label-specific
features and label correlation while they ignore the common features and instance …

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 …

It is of utmost importance for marketing academics and service industry practitioners to
understand the factors that influence customer satisfaction. This study proposes a novel …

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm.
However, multi-view multi-label data in the real world is commonly incomplete due to the …