In the recent decades, researchers have introduced an abundance of feature selection
methods many of which are studied and analyzed over the high dimensional datasets …

Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in
solving combinatorial and NP-hard optimization problems in various research fields …

Feature selection (FS) is an important preprocessing technique for improving the quality of
feature sets in many practical applications. Particle swarm optimization (PSO) has been …

Abstract “Dimensionality” is one of the major problems which affect the quality of learning
process in most of the machine learning and data mining tasks. Having high dimensional …

The “curse of dimensionality” is one of the largest problems that influences the quality of the
optimization process in most data mining, pattern recognition, and machine learning tasks …

Microarray gene expression based medical data classification has remained as one of the
most challenging research areas in the field of bioinformatics, machine learning and pattern …

In recent times, microarray gene expression datasets have gained significant popularity due
to their usefulness to identify different types of cancer directly through bio-markers. These …

In recent years, Jaya optimization algorithm has been successfully applied in several
optimization problems. This paper presents a novel feature selection (FS) approach based …

Differential evolution has started to attract a lot of attention as a powerful search method and
has been successfully applied to a variety of applications including pattern recognition. One …

Support vector machines (SVM) are one of the important techniques used to solve
classifications problems efficiently. Setting support vector machine kernel factors affects the …