Explicit and size-adaptive PSO-based feature selection for classification

Qu, Liangdong; He, Weibin; Li, Jianfei; Zhang, Hua; Yang, Cheng; Xie, Bo

doi:10.1016/j.swevo.2023.101249

Cited by 30 publications

(10 citation statements)

References 30 publications

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“…Recently, GWO has been extensively used to solve feature selection problems in diverse areas, including benchmarks problems as in [5,40,48], diagnosis of Parkinson's disease [49,50], facial emotion recognition facial [51], face recognition [52], EMG signals classification [53], diagnosis of Paraquat disease [54], Coronary Artery disease classification [23], medical diagnosis [55] and intrusion detection [56,57]. A comprehensive review that includes publications from 2015 up to 2019 on feature selectionbased GWO is presented in a published book chapter [6], where interested readers on GWO feature selection can find a detailed state-of-the-art.…”

Section: State-of-the-art Feature Selection Methodsmentioning

confidence: 99%

“…Careful execution of this step is essential because any mistakes can make it difficult to extract useful insights. One aspect of data preprocessing is feature selection, which involves using various strategies to remove irrelevant and redundant attributes from a given dataset [5,6]. In a dataset consisting of instances like emails, patients, communication attempts, user accounts, images, etc., features refer to the attributes associated with each instance.…”

Section: Introductionmentioning

confidence: 99%

“…GWO has lower computationally cost than other metaheuristic algorithms such as PSO and GA and can converge faster. As a result, GWO has been utilized as an efficient algorithm in several domains, including feature selection [5,23,24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Al-Tashi¹,

Shami²,

Abdulkadir³

et al. 2023

Computer Systems Science and Engineering

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Section: State-of-the-art Feature Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Al-Tashi¹,

Shami²,

Abdulkadir³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

“…It is suggested that the heuristic algorithm combined with the wrapper-based method owns a higher research value [2,3]. Qu et al [4] combined Particle Swarm Optimization (PSO) with a wrapper approach for classification. Zhou et al [5] proposed a new wrapper feature selection method based on a correlation-guided Genetic Algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

“…Qu et al. [4] combined Particle Swarm Optimization (PSO) with a wrapper approach for classification. Zhou et al.…”

Section: Introductionmentioning

confidence: 99%

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Wang

et al. 2023

IET Control Theory & Appl

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Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.

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A many objective based feature selection model for software defect prediction

Mao,

Zhang,

Zhao

et al. 2024

Concurrency and Computation

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SummaryGiven the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity.

show abstract

Explicit and size-adaptive PSO-based feature selection for classification

Cited by 30 publications

References 30 publications

Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

A many objective based feature selection model for software defect prediction

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