As an essential preprocessing step, the feature selection for multi-label classification is an
efficient tool to solve the high-dimensionality in training data under multiple semantics …

Label distribution learning (LDL), focusing on the relative importance of different labels to
the instance, is proposed for solving label ambiguity problem in recent years. However, for …

Class labels in multi-label datasets are only associated with a very small fraction of the data
instances leading to a class imbalance problem. There exist multi-label learning algorithms …

In multi-label learning algorithms, the classification performance can be significantly
improved using global and local label correlation. However, the incompleteness of the label …

Label correlations have always been one of the hotspots of multi-label learning. Using label
correlations to complete the original label can enrich the information of the label matrix. At …

In label distribution learning, high dimensionality is one of the most prominent characteristics
of the data, which increases the model complexity and computational cost. Feature selection …

Recently, multilabel classification is of increasing interest in machine learning and artificial
intelligence. However, the distances of samples in most Relief methods easily result in …

Label distribution learning (LDL) is an interpretable and general learning paradigm that has
been applied in many real-world applications. In contrast to the simple logical vector in …

Multi-labels are more suitable for the ambiguity of the real world. However, missing labels
are common in multi-label learning datasets; this results in unbalanced labeling and label …

In conventional multi-label learning, each training instance is associated with multiple
available labels. Nevertheless, real-world objects usually exhibit more sophisticated …