With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

The collection of large-scale datasets inevitably introduces noisy labels, leading to a
substantial degradation in the performance of deep neural networks (DNNs). Although …

Multi-label feature selection, which addresses the challenge of high dimensionality in multi-
label learning, has wide applicability in pattern recognition, machine learning, and related …

Multi-label learning tasks usually encounter the problem of the class-imbalance, where
samples and their corresponding labels are non-uniformly distributed over multi-label data …

Feature selection can alleviate the problem of the curse of dimensionality by selecting more
discriminative features, which plays an important role in multi-label learning. Recently …

Multilabel feature selection solves the dimension distress of high-dimensional multilabel
data by selecting the optimal subset of features. Noisy and incomplete labels of raw …

Multi-label learning is a class of machine learning algorithms that study the classification
problem of data associated with multiple labels simultaneously. Ensemble-based method is …

Feature selection plays a key role in preprocessing, effectively addressing the curse of
dimensionality in multi-label learning. While current approaches commonly utilize feature or …

As a cognitive process, concept-cognitive learning (CCL) emphasizes the structured
expression of data through systematic cognition and understanding, to obtain valuable …

In recent studies, existing multilabel feature selection models have focused on either
considering the relationship between labels or the redundancy between features …