This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle
Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses …

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 …

In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to
deal with Feature Selection (FS) problems. FS is an important data reduction step in data …

Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the
hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) …

In many data analysis tasks, one is often confronted with very high dimensional data.
Feature selection techniques are designed to find the relevant feature subset of the original …

An intrusion detection system has become a vital mechanism to detect a wide variety of
malicious activities in the cyber domain. However, this system still faces an important …

Identification of the optimal subset of features for Feature Selection (FS) problems is a
demanding problem in machine learning and data mining. A trustworthy optimization …

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for
the Feature Selection (FS) problem. FS is an essential machine learning and data mining …

Feature Selection (FS) is a major preprocessing stage which aims to improve Machine
Learning (ML) models' performance by choosing salient features, while reducing the …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …