A comprehensive survey on recent metaheuristics for feature selection

Dökeroğlu, Tansel; Deniz, Ayça; Kızılöz, Hakan Ezgi

doi:10.1016/j.neucom.2022.04.083

Cited by 190 publications

(79 citation statements)

References 251 publications

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“…The other is the feature selection based on search. In this kind of algorithm, a heuristic search method is often used to find the optimal feature subset [ 15 ]. The feature subset selected in this way guarantees the common influence of the features on the classification target.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, due to the excellent global search ability and versatility of evolutionary algorithms, many researchers have focused on searching feature spaces by improving various evolutionary algorithms. Dökeroğlu et al [ 15 ] applied the backbone particle swarm algorithm combined with the nearest neighbor algorithm to feature selection. Dökeroğlu et al [ 15 ] used decision trees for feature selection and used genetic algorithms to find a set of feature subsets that minimized the classification error rate of decision trees.…”

Section: Introductionmentioning

confidence: 99%

“…Dökeroğlu et al [ 15 ] applied the backbone particle swarm algorithm combined with the nearest neighbor algorithm to feature selection. Dökeroğlu et al [ 15 ] used decision trees for feature selection and used genetic algorithms to find a set of feature subsets that minimized the classification error rate of decision trees. Liu et al [ 16 ] introduced three new initialization mechanisms, individual and global optimal update mechanisms in particle swarm optimization, which improved both the number of features and the classification performance.…”

Section: Introductionmentioning

confidence: 99%

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PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

Praveen

Tyagi²,

Singh

et al. 2022

BioMed Research International

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Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient’s internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

Praveen

Tyagi²,

Singh

et al. 2022

BioMed Research International

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“…Considering the importance of feature selection for intelligent systems, many different research have been proposed in this field. Miao and Niu 4 and Dokeroglu et al 23 reviews some of the important feature selection algorithms for intelligent systems. Regarding the dimensions of search space, many different research use meta‐heuristic algorithms as a wrapper technique to select the best subset of features.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

Riyahi

Rafsanjani

Gupta

et al. 2022

Int J of Intelligent Sys

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With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading

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“…A filter-based method is independent of any prediction model. A wrapper-based method is based on an optimization algorithm and a forecasting model [ 41 ]. In terms of this study, DL methods can automatically perform feature extraction, but at a high cost.…”

Section: Related Workmentioning

confidence: 99%

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Liu

Han

et al. 2022

Entropy

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This paper addresses the problem of frequency stability prediction (FSP) following active power disturbances in power systems by proposing a vision transformer (ViT) method that predicts frequency stability in real time. The core idea of the FSP approach employing the ViT is to use the time-series data of power system operations as ViT inputs to perform FSP accurately and quickly so that operators can decide frequency control actions, minimizing the losses caused by incidents. Additionally, due to the high-dimensional and redundant input data of the power system and the O(N2) computational complexity of the transformer, feature selection based on copula entropy (CE) is used to construct image-like data with fixed dimensions from power system operation data and remove redundant information. Moreover, no previous FSP study has taken safety margins into consideration, which may threaten the secure operation of power systems. Therefore, a frequency security index (FSI) is used to form the sample labels, which are categorized as “insecurity”, “relative security”, and “absolute security”. Finally, various case studies are carried out on a modified New England 39-bus system and a modified ACTIVSg500 system for projected 0% to 40% nonsynchronous system penetration levels. The simulation results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on normal, noisy, and incomplete datasets in comparison with eight machine-learning methods.

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A comprehensive survey on recent metaheuristics for feature selection

Cited by 190 publications

References 251 publications

PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

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