The transportation sector is deemed one of the primary sources of energy consumption and
greenhouse gases throughout the world. To realise and design sustainable transport, it is …

Computer systems store massive amounts of data with numerous features, leading to the
need to extract the most important features for better classification in a wide variety of …

The study presents a novel framework integrating feature selection (FS) and machine
learning (ML) techniques to forecast inland national energy consumption (EC) in the United …

Feature selection is a common data preprocessing technique that aims to construct better
models by selecting the most predictive features. Existing particle swarm optimization-based …

Partial label learning is a multi-class classification issue in which each training instance is
associated with a set of candidate labels. Feature selection is an effective method to improve …

Various evolutionary computation algorithms have shown their excellent performance for
high-dimensional feature selection (FS). However, most of current FS methods choose a …

Feature selection has a prominent role in high-dimensional datasets to increase
classification accuracy, decrease the learning algorithm computational time, and present the …

Knowledge discovery of heterogeneous data is an active topic in knowledge engineering.
Feature selection for heterogeneous data is an important part of effective data analysis …

In recent years, several screening methods have been published for ultrahigh-dimensional
data that contain hundreds of thousands of features, many of which are irrelevant or …

Filters and wrappers represent two mainstream approaches to feature selection (FS).
Although evolutionary wrapper-based FS outperforms filters in addressing real-world …