Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

Feature selection (FS) may improve the performance, cost-efficiency, and understandability
of supervised machine learning models. In this paper, FS for the recently introduced …

State of health (SOH) estimation is essential for lithium-ion battery systems to ensure safe
and reliable operation. The existing SOH estimation considers a few available signals, such …

Fault diagnosis based on artificial intelligence methods is a promising tool to eliminate
reliance on a priori knowledge. Sparsity is an increasingly important topic in the field of …

Multi-label feature selection is a hot topic in multi-label high-dimensional data processing.
However, some multi-label feature selection models use manifold graphs. Due to its fixed …

Feature selection is an important dimensionality reduction technique in machine learning,
pattern recognition, image processing, and data mining. Most existing feature selection …

Decision variable analysis (DVA) methods have provided the promising direction in solving
large-scale multiobjective optimization problems (LMOPs). However, most existing DVA …

Graph-based sparsity learning is one of the most successful unsupervised feature selection
methods that has been widely adopted in many real-world applications. However, traditional …

With the rapid advances of deep learning-based computer vision (CV) technology, digital
images are increasingly consumed, not by humans, but by downstream CV algorithms …

Feature selection in machine learning as a key data processing technique has two
conflicting goals: minimizing the classification error rate and minimizing the number of …