Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …

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 …

With the increase of the number of features and the sample size, existing feature selection
(FS) methods based on evolutionary optimization still face challenges such as the “curse of …

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 …

Reducing the number of selected features and improving the classification performance are
two major objectives in feature selection, which can be viewed as a multi-objective …

Evolutionary multiobjective feature selection (FS) has gained increasing attention in recent
years. However, it still faces some challenges, for example, the frequently appeared …

The “curse of dimensionality” brings new challenges to the feature selection (FS) problem,
especially in bioinformatics filed. In this paper, we propose a hybrid Two-Stage Teaching …

Feature selection (FS) is increasingly important in classification tasks. Although
metaheuristic algorithms have been extensively utilized for FS problems, they share the …