Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Al-Tashi, Qasem; Abdulkadir, Said Jadid; Rais, Helmi Md; Mirjalili, Seyedali; Alhussian, Hitham; Ragab, Mohammed Gamal; Alqushaibi, Alawi

doi:10.1109/access.2020.3000040

Cited by 87 publications

(30 citation statements)

References 54 publications

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“…Moreover, the author proposed a binary version (BMOGW-S) by using a sigmoidal function for solving multi-objective feature selection problem in which the artificial neural network was used for classification. BMOGW-S applied to fifteen benchmark datasets and compared with MOGWO with tanh transfer function [175]. Hu et al [176] proposed new transfer functions and new updating scheme for parameters of GWO.…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

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

et al. 2021

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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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Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

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

et al. 2021

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“…Therefore, a multi-objective idea of these techniques has been adapted to solve the problem of feature selection and shows a great success. Such methods include MOGA [20], [21], MOPSO [22], MOGWO [23], [24] etc. However, these multiobjective techniques not fully investigated.…”

Section: ) Search Techniquesmentioning

confidence: 99%

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

Al-Tashi¹,

et al. 2020

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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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“…The gray wolf optimizer (GWO) is an intelligent optimization algorithm that was developed as an optimized search method inspired by gray wolf predation activities. It has been widely considered by scholars for its strong convergence performance, few parameters, and easy realization and has been commonly applied to parameter optimization, image classification, and other fields [36,37]. Given the advantages of the GWO, we used the GWO optimization method to optimize parameters of regularization parameters γ and Gaussian kernel parameters σ for the LSSVM prediction model in this study.…”

Section: Parameter Optimization For Lssvmmentioning

confidence: 99%

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

et al. 2020

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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Cited by 87 publications

References 54 publications

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

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

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

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

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