Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims
to remove redundant and uncorrelated features, thus improving the accuracy of models …

Evolutionary algorithms have been widely used for tackling multi-objective optimization
problems, while feature selection in classification can also be seen as a discrete bi-objective …

Feature selection can be treated as a bi-objective optimization problem, if aimed at
minimizing both classification error and number of selected features, suitable for multi …

The charging process design is crucial for optimizing the performance of lithium-ion batteries
by identifying protocols that meet diverse demands. The main challenges include: 1) the …

In the context of increasing data scale, contemporary optimization algorithms struggle with
cost and complexity in addressing the feature selection (FS) problem. This paper introduces …

Feature selection (FS) is an essential pre-processing technique for high-dimensional data.
Wrapper-based FS techniques are known for their superior performance over filter FS …

This study focuses on dimensionality reduction by variable selection in business failure
prediction models. A new method of dimensionality reduction by variable selection using …

The feature subset obtained by traditional feature selection algorithms usually contains
many irrelevant features and redundant features, which increases the size of the feature set …

As an increasing number of intelligent decision-making problems of vehicles are addressed
using implementations of deep learning (DL) methods, the interpretability of intelligent …

Online streaming feature selection (OSFS), as an online learning manner to handle
streaming features, is critical in addressing high‐dimensional data. In real big data‐related …