Multi-label learning generalizes traditional learning by allowing an instance to belong to
multiple labels simultaneously. This causes multi-label data to be characterized by its large …

Multi-label feature selection involves the selection of relevant features from multi-labeled
datasets, resulting in a potential improvement of multi-label learning accuracy. In …

Multi-label learning has been extensively studied in many areas such as information
retrieval, bioinformatics, and multimedia annotation. However, multi-label datasets often …

Multilabel learning is a challenging task demanding scalable methods for large-scale data.
Feature selection has shown to improve multilabel accuracy while defying the curse of …

Nowadays, many data sources that include multi-label learning and multi-label classification
have emerged in recent application areas. To achieve high classification accuracy, the multi …

Feature selection is widely used in multi-label classification because of its simplicity,
efficiency, and accuracy. Specifically, evolutionary algorithm (EA)-based multi-label feature …

Multi-label feature selection that identifies important features from the original feature set of
multi-labeled datasets has been attracting considerable attention owing to its generality …

Multilabel feature selection involves the selection of relevant features from multilabeled
datasets, resulting in improved multilabel learning accuracy. Evolutionary search‐based …

Multi-label feature selection is designed to select a subset of features according to their
importance to multiple labels. This task can be achieved by ranking the dependencies of …

The data‐driven management of real‐life systems based on a trained model, which in turn is
based on the data gathered from its daily usage, has attracted a lot of attention because it …