Quantum based Whale Optimization Algorithm for wrapper feature selection

Agrawal, R. K.; Kaur, Baljeet; Sharma, Surbhi

doi:10.1016/j.asoc.2020.106092

Cited by 139 publications

(53 citation statements)

References 33 publications

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“…Twenty benchmark datasets have been utilized in this approach. Agrawal et al [204] proposed a new version of WOA that is based on quantum concepts in which, quantum bit representation was used for all individuals. And the new version was applied to fourteen datasets.…”

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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“…Taradeh et al [77] proposed an evolutionary gravitational search-based method for selecting relevant features. Agrawal et al [78] anticipated a quantum-based WOA for reducing extraneous and unnecessary features from the datasets. Ouadfel et al [79] used an improved CSA to select the optimal features.…”

Section: Literature Reviewmentioning

confidence: 99%

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

et al. 2021

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“…Due to the ability of meta-heuristic algorithms to solve many real problems [16]- [19] in less time with higher accuracy, they are widely used for FS to resolve a time complexity problem required by the traditional techniques such as mutual information, information gain, relief, depth search, and breadth search. Recently, several meta-heuristic algorithms have been proposed for tackling this problem, such as gradient descent algorithm (GDA) [20], tabu search [21], quantum-based whale optimization algorithm (QWOA) [22], improved binary sailfish optimizer (BFO) [23], novel chaotic crow search algorithm (CCSA) [24], novel chaotic selfish herd optimizer (CSHO) [25], chaotic dragonfly algorithm (CDFA) [26], and fish swarm optimization (FSO) [27], Sshaped binary whale optimization algorithm (BWOA) [28], grey wolf optimizer with a two-phase mutation strategy (GWOTM) [29], binary particle swarm optimization with time-varying inertia weight strategies [30], Gaussian mutational chaotic fruit fly-built optimization (MCFOA) [31], and a discrete binary version of the particle swarm optimization (BPSO) [32]. Major applications of those algorithms are surveyed within the rest of this section.…”

Section: Introductionmentioning

confidence: 99%

An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection

Abdel‐Basset

Sallam²,

Mohamed

et al. 2021

IEEE Access

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This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms.INDEX TERMS Grey-wolf optimizer, feature selection, simulated annealing, mutation strategy.

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Quantum based Whale Optimization Algorithm for wrapper feature selection

Cited by 139 publications

References 33 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)

bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection

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