A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.

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Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection

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.

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

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) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values. This updating mechanism can be improved to utilize the survival-of-the-fittest principle by adopting different functions such as linear, quadratic, and sinusoidal. In this paper, a novel BDA is proposed. The algorithm uses different strategies to update the values of its five main coefficients to tackle Feature Selection (FS) problems. Three versions of BDA have been proposed and compared against the original DA. The proposed algorithms are Linear-BDA, Quadratic-BDA, and Sinusoidal-BDA. The algorithms are evaluated using 18 well-known datasets. Thereafter, they are compared in terms of classification accuracy, the number of selected features, and fitness value. The results show that Sinusoidal-BDA outperforms other proposed methods in almost all datasets. Furthermore, Sinusoidal-BDA exceeds three swarm-based methods in all the datasets in terms of classification accuracy and it excels in most datasets when compared in terms of the fitness function value. In a nutshell, the proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling FS problems.

An improved Dragonfly Algorithm for feature selection

Feature selection is imperative in machine learning and data mining when we have high-dimensional datasets with redundant, nosy and irrelevant features. The area of feature selection deals reducing the dimensionality of data and selecting only the most relevant features to increase the classification performance and reduce the computational cost. This problem has exponential growth, which makes it challenging specially for datasets with a large number of features. To solve this problem, a wide range of optimization algorithms are used of which grey wolf optimizer (GWO) is a recent one. This book chapter provides a brief review of the latest works on feature selection using GWO.

A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification

Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.

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Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Cardiovascular disease has been declared as one of the deadly illness that affects humans in the Middle and Old ages across the globe. One of the cardiovascular disease known as Coronary artery, has recorded the highest number of motility rates in the recent years. Machine learning tools have been very effective in investigating the causes of such lethal disease which involve analyzing large amount of dataset. Such datasets might contain redundant and irrelevant features which affect the classification accuracy and processing speed. Hence, applying feature selection technique for the elimination of the said redundant and irrelevant features is necessary. In this paper, a novel wrapper feature selection method is proposed to determine the optimal feature subset for diagnosing coronary artery disease. This proposed method consists of two main stages feature selection and classification. In the first stage, Grey Wolf Optimization (GWO) is used to find the best features in the disease identification dataset. In the second stage, the fitness function of GWO is evaluated using Support Vector Machine classifier (SVM). Cleveland Heart disease dataset is used for performance validation of the proposed method. The experimental results showed that, the proposed GWO-SVM outperforms current existing approaches with an achievement of 89.83% in accuracy, 93% in sensitivity and 91% in specificity rates.

Feature selection method based on grey wolf optimization for coronary artery disease classification

High dimensionality is a data quality problem that negatively influences the predictive capabilities of prediction models in software defect prediction (SDP). As a viable solution, feature selection (FS) has been used to address the high dimensionality problem in SDP. From existing studies, Filter-based feature selection (FFS) and Wrapper Feature Selection (WFS) are the two basic types of FS methods. WFS methods have been regarded to have superior performance between the two. However, WFS methods have been known to have high computational cost as the number of executions required for feature subset search, evaluation and selection is not known prior. This often leads to overfitting of prediction models due to easy trapping in local maxima. Applying appropriate search method in WFS subset evaluator phase can resolve its trapping in local maxima. Best First Search (BFS) and Greedy Step-wise Search (GSS) methods have been extensively and conventionally used as viable search methods in WFS with positive impacts. However, metaheuristic search methods can also be as effective as BFS and GSS. Consequently, this study conducts an empirical comparative analysis of 13 search methods (11 state-of-the-art metaheuristic search and 2 conventional search methods) in WFS methods for SDP. The experimental results showed that metaheuristic (AS, BS, BAT, CS, ES, FS, FLS, GS, NSGA-II, PSOS, RS) as search methods in WFS proved to be better than conventional search methods (BFS and GSS). Although the average computational time of metaheuristic-based WFS methods is relatively high. We recommend that metaheuristic search can be used as alternate search methods for WFS methods in SDP.

Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis

Summary By measuring the parameter uncertainty of the deep neural net. weights and biases by adding an additional Monte Carlo Dropout layer, the proposed Monte Carlo Dropout based Deep Neural Net. quantifies the uncertainty in the process of facies classification using seismic data and outputs a better classification than the traditional Deep Learning model. 

Said Jadid

Papers

Feature selection method based on grey wolf optimization for coronary artery disease classification

Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis

A Monte Carlo Dropout Based Deep Learning Model to Quantify Uncertainty in Facies Classification