A dandelion algorithm (DA) is a recently developed intelligent optimization algorithm for
function optimization problems. Many of its parameters need to be set by experience in DA …

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 …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Many machine learning methods have been proposed to achieve accurate transaction fraud
detection, which is essential to the financial security of individuals and banks. However …

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 …

Dataset scaling, aka normalization, is an essential preprocessing step in a machine learning
(ML) pipeline. It aims to adjust the scale of attributes in a way that they all vary within the …

Causal feature selection as an emerging topic has drawn increasing attention in the field of
causal discovery and machine learning. However, existing causal feature selection …

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 …

The proliferation of Internet of Things (IoT) and the adoption of Edge Computing paradigms
have led to a substantial increase in data volume. Data imbalance is inevitable in distributed …