Feature selection is to reduce both the dimensionality of data and the classification error rate
(ie, increase the classification accuracy) of a learning algorithm. The two objectives are often …

Feature selection aims to reduce the number of features and improve the classification
accuracy, which is an essential step in many real-world problems. Multiple feature subsets …

Modern data collection technologies may produce thousands of or even more features in a
single dataset. The high dimensionality of data poses a barrier to determining discriminating …

By selecting a small subset of relevant features, feature selection can reduce the
dimensionality of the problem while maintaining or increasing the discriminating ability of the …

Feature selection aims to select a small subset of relevant features while maintaining or
even improving the classification performance over using all features. Feature selection can …

The main aim of feature subset selection is to find the minimum number of required features
to perform classification without affecting the accuracy. It is one of the useful real-world …

Emotion categorization plays an important role in understanding human emotions by
artificial intelligence systems such as robots. It is a difficult task as humans express many …

This study aims to investigate the behaviour and applications of an Evolutionary Algorithm
(EA) based on a particular approach of Cooperative Co-evolution Algorithm (CCEA), the …

❖ Mengjie Zhang is currently Professor of Computer Science, Head of the Evolutionary
Computation Research Group, and the Associate Dean (Research and Innovation) in the …

The quality of the data space, which is often represented by a set of features, is one of the
most critical aspects affecting the classification performance of a machine learning …