A Survey on Feature Selection Techniques using Evolutionary Algorithms

Abstract

Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the preferred feature matters in complex and massive data. Here, furthermore, I discuss algorithms like the genetic algorithm (GA), the Non-Dominated Sorting Genetic Algorithm (NSGA-II), Particle Swarm Optimization (PSO), and some other meta-heuristic strategies for considering the provisional separation of issues. A comparison of these algorithms has been performed; the results show that the feature selection technique benefits machine learning algorithms by improving the performance of the algorithm. This paper also presents various real-world applications of using feature selection.

The Effect of Meta-heuristic Methods on the Performance of ImageClassification

“…According to the literature, meta-heuristic methods such as evolution-based, swarm-based, physics-based, and human-based methods are employed as FS. Meta-heuristic approaches balance exploration and exploitation phases to avoid convergence or being stuck in local optima [17], [18]. It is possible to use meta-heuristics alone or in combination with other methods to select features, and both ways provide better results than traditional methods.…”

Optimization Techniques for Human Multi-Biometric Recognition System

“…Consequently, in this research, metaheuristic optimization algorithms, such as Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization, were implemented. Based on feature selection(optimization)methods, features that are more marked are selected, which helps in building a good classifier model [21]. The acquired optimal features are forwarded as inputs to the classifier algorithm.…”

Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder

“…Numerous studies on feature selection (FS) techniques have been conducted recently to create a dataset with the most pertinent features for the best model performance [18] and reduce computation time [19]. FS is typically used to select only efficient features based on the given input by reducing noisy data, which aids in the identification of the application [20]. There have been many reports in previous research on FS methods that affect improving ML performance, including relief [21], minimum-redundancy-maximum-relevance FS (m-RMR) [22], information gain [23]- [25], and gain ratio (GR) [23], [26], [27].…”