Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Abstract

Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem

“…The above-mentioned algorithms are able to provide better solutions for most of the feature selection problems [33], [34]. However, these algorithms are unable to provide optimal feature subset for high dimensional datasets.…”

A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification

“…e dataset for solving Android malware classification in this paper is divided into two parts: one is obtained by decompiling, with a total of 2720 samples, 532 malicious application samples from the Canadian Institute of Network Security [41], which contain typical Android platform malware, such as ransomware malicious applications, threatening SMS applications, and advertising (3) for each nest in population do (4) for each dimension in one nest do (5) Randomly assign values to two populations of individuals based on [Lb, Ub]. Updating populations based on chaotic maps equations ( 7) and (8) ( 6) end (7) Convert two populations to binary using equations ( 3) and (4) (8) Calculate the fitness for individuals of two populations by equation ( 12) (9) Update the nest_fitness ( 10) end (11) Both populations perform Algorithm 3 (12) while t ≤ max_iterations or stop criteria do (13) for each nest in populations do (14) perform levy flights to generate new populations use equation ( 9) and (10) ( 15) end (16) for each nest in populations do (17) ⊳ Rectification procedure (18) Normalize the population value generated by Lévy flight (19) Binary conversion ( 20) end (21) en execute Algorithm 2 ( 22) end (23) if nest_fitness < mut_fitness then (24) repalce the nest with mut_pop(i) ( 25 applications, and 400 malicious samples from Dr. Wang's repository dataset (http://infosec.bjtu.edu.cn/wangwei/? page_id�85); benign apps were mainly downloaded through Google Play Python crawlers to obtain a total of 188 APKs from the Xiaomi App Market, which were released to the App Market after security testing.…”

“…Traditional wrapper methods are based on improvements of machine learning classifiers, greedy search, and other methods [6], but these methods tend to fall into local optima and are computationally expensive. Nowadays, nature-inspired algorithms are widely used to solve optimization problems, which are inspired by nature and are increasingly used by scholars to find the optimal value in the problem due to their conceptual simplicity and ease of implementation [7]. A large number of contributions have been made using natureinspired algorithms to solve feature selection problems.…”

A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

“…In this sense, one can apply various heuristic search strategies such as hill climbing and best first [25] to search the feature subset space in a reasonable time. Metaheuristic algorithms such as Simulated Annealing (SA) [29], Genetic Algorithm (GA) [30], and Particle Swarm Optimization (PSO) [31] have also been applied efficiently as search-based feature selection approaches. Recently, researchers have explored strategies that design parallel algorithms to improve the running time of their feature selection approach, as proposed by Huang et al [32] for internet text classification.…”