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

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …

Feature selection is an important data preprocessing method. This paper studies a new multi-
objective feature selection approach, called the Binary Differential Evolution with self …

Feature selection (FS) is an important data processing technique in the field of machine
learning. There have been various FS methods, but all assume that the cost associated with …

In the last two decades, a variety of different types of multiobjective optimization problems
(MOPs) have been extensively investigated in the evolutionary computation community …

Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary
algorithms to approximate the optimal solutions of large-scale multiobjective optimization …

Feature selection has two major conflicting aims, ie, to maximize the classification
performance and to minimize the number of selected features to overcome the curse of …

Abstract Dimensionality reduction or Feature Selection (FS) is a multi-target optimization
problem with two goals: improving the classification efficiency while simultaneously …

Classification problems often have a large number of features in the data sets, but not all of
them are useful for classification. Irrelevant and redundant features may even reduce the …

In classification, feature selection is an important data pre-processing technique, but it is a
difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is …