A meta-heuristic feature selection algorithm combining random sampling accelerator and ensemble using data perturbation

Meta-heuristic algorithms have been extensively utilized in feature selection tasks because they can obtain the global optimal solution. However, the meta-heuristic algorithm will take too much time in the face of a large number of samples. Although most of the studies compromise to approximate opti...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 24; s. 29781 - 29798
Hlavní autori:	Zhang, Shuaishuai, Liu, Keyu, Xu, Taihua, Yang, Xibei, Zhang, Ao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2023 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Classification Computer Science Datasets Feature selection Heuristic Heuristic methods Machines Manufacturing Mechanical Engineering Perturbation Processes Random sampling Stability Feature selection Neighborhood rough set Meta-heuristic Random sampling Data perturbation
ISSN:	0924-669X, 1573-7497
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Abstract	Meta-heuristic algorithms have been extensively utilized in feature selection tasks because they can obtain the global optimal solution. However, the meta-heuristic algorithm will take too much time in the face of a large number of samples. Although most of the studies compromise to approximate optimal solutions for avoiding time-consuming problems, a new problem with reduced classification performance, especially classification stability, is then generated. Aiming to above problems, this paper proposes a new feature selection framework. First, this framework exploits a voting ensemble strategy to improve classification stability by reducing the impact of misclassified labels on the overall classification results. Second, the framework uses a data perturbation strategy to enhance classification accuracy. In particular, the data perturbation strategy is able to generate more neighborhood relationships in the dataset, which could reveal the distribution of various features of the samples. A voting ensemble of different feature distributions is capable of extracting more information from the dataset, then the initially misclassified samples are more likely to be returned to the correct classification. Third, the framework takes a random sampling accelerator into account to solve the problem of excessive time consumption by reducing the size of the search sample space. Finally, for the sake of verifying the effectiveness of the proposed framework, four meta-heuristic feature selection methods based on a neighborhood rough set are compared on 20 datasets. The experimental results indicate that our framework could improve classification performance and accelerate feature selection, particularly in confronting large sample sizes.
AbstractList	Meta-heuristic algorithms have been extensively utilized in feature selection tasks because they can obtain the global optimal solution. However, the meta-heuristic algorithm will take too much time in the face of a large number of samples. Although most of the studies compromise to approximate optimal solutions for avoiding time-consuming problems, a new problem with reduced classification performance, especially classification stability, is then generated. Aiming to above problems, this paper proposes a new feature selection framework. First, this framework exploits a voting ensemble strategy to improve classification stability by reducing the impact of misclassified labels on the overall classification results. Second, the framework uses a data perturbation strategy to enhance classification accuracy. In particular, the data perturbation strategy is able to generate more neighborhood relationships in the dataset, which could reveal the distribution of various features of the samples. A voting ensemble of different feature distributions is capable of extracting more information from the dataset, then the initially misclassified samples are more likely to be returned to the correct classification. Third, the framework takes a random sampling accelerator into account to solve the problem of excessive time consumption by reducing the size of the search sample space. Finally, for the sake of verifying the effectiveness of the proposed framework, four meta-heuristic feature selection methods based on a neighborhood rough set are compared on 20 datasets. The experimental results indicate that our framework could improve classification performance and accelerate feature selection, particularly in confronting large sample sizes.
Author	Xu, Taihua Liu, Keyu Zhang, Ao Zhang, Shuaishuai Yang, Xibei
Author_xml	– sequence: 1 givenname: Shuaishuai surname: Zhang fullname: Zhang, Shuaishuai organization: School of Computer, Jiangsu University of Science and Technology – sequence: 2 givenname: Keyu surname: Liu fullname: Liu, Keyu organization: School of Computing and Artificial Intelligence, Southwest Jiaotong University – sequence: 3 givenname: Taihua orcidid: 0000-0001-5241-6647 surname: Xu fullname: Xu, Taihua email: xth19890410@163.com organization: School of Computer, Jiangsu University of Science and Technology, Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University – sequence: 4 givenname: Xibei surname: Yang fullname: Yang, Xibei organization: School of Computer, Jiangsu University of Science and Technology – sequence: 5 givenname: Ao surname: Zhang fullname: Zhang, Ao organization: School of Computer, Jiangsu University of Science and Technology
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Cites_doi	10.1016/j.patrec.2016.03.002 10.1016/j.knosys.2017.10.033 10.1016/j.knosys.2020.106014 10.1109/TFUZZ.2022.3216990 10.1016/j.ijar.2019.12.001 10.3233/IFS-222910 10.1016/j.ijar.2018.11.010 10.1007/s12559-022-10022-6 10.1007/s13042-019-00959-w 10.1007/s00500-021-06424-7 10.1016/j.disopt.2017.12.001 10.1016/j.eswa.2006.10.043 10.1109/TETCI.2020.2998919 10.1109/TMI.2020.3046692 10.1016/S0020-0255(98)10006-3 10.1609/aaai.v35i13.17362 10.1007/s13042-022-01618-3 10.4018/IJISP.2020070106 10.1109/TKDE.2019.2959988 10.1109/TBME.2021.3113593 10.1016/j.ins.2019.01.033 10.1109/TNNLS.2022.3184120 10.1109/TCSI.2021.3118301 10.1016/j.ijar.2021.09.016 10.1109/ACCESS.2022.3162074 10.1109/TFUZZ.2021.3096212 10.1007/s00521-015-1923-y 10.1016/j.dam.2020.06.020 10.1016/j.ins.2019.01.041 10.1109/TFUZZ.2022.3217377 10.1016/j.asoc.2018.05.013 10.1016/j.ins.2022.12.074 10.1016/j.jocs.2023.101942 10.1109/TII.2020.3007419 10.1109/CVPR.2019.00723 10.3390/sym14091828 10.1016/j.ins.2019.07.051 10.1007/978-3-540-87563-5_6 10.1109/TKDE.2010.254 10.1016/j.ijar.2010.01.004 10.1109/TFUZZ.2020.3001670 10.1007/s13042-019-00996-5 10.1016/j.ins.2016.04.005 10.3390/math10040553 10.1016/S0020-0255(02)00197-4 10.1109/ACCESS.2019.2926799 10.1016/j.ins.2013.12.043 10.1109/ACCESS.2020.2970438 10.1016/j.ins.2008.05.024 10.1109/TFUZZ.2019.2955047 10.1109/TGRS.2021.3129841 10.1016/j.ijar.2021.06.005 10.1016/j.neucom.2015.06.090 10.1016/j.patcog.2016.05.012
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References	Xu, Wang, Yang (CR28) 2020; 118 Tawhid, Ibrahim (CR27) 2020; 11 Dong, Wang, Chen (CR2) 2023; 467 Chen, Liu, Yang, Fujita (CR47) 2022; 140 Hu, Yu, Liu, Wu (CR33) 2008; 178 Yao, Zhang, Wang (CR59) 2008; 5150 Liu, Li, Yang, Yang, Liu (CR5) 2022; 131 Aksakalli, Malekipirbazari (CR62) 2016; 75 Ding, Triguero, Lin (CR11) 2021; 5 Chen, Lin, Mi, Li, Ding (CR4) 2022; 30 Wei, Wu, Liang, Cui, Sun (CR14) 2018; 140 Yan, Ba, Xu, Yu, Shi, Han (CR13) 2022; 10 Zou, Li, Jiang, Yang (CR38) 2019; 7 Campagner, Ciucci, Hüllermeier (CR25) 2021; 136 CR9 Yang, Liang, Yu (CR32) 2019; 105 CR48 CR46 Qian, Han, Yu, Liu (CR31) 2023; 44 Ismail, Sandell (CR6) 2022; 69 Etesami, Haemers (CR16) 2020; 285 Gong, Liu, Xu, Wang, Yang (CR43) 2022; 13 Hu, Zhang, Chen, Pedrycz, Yu (CR52) 2010; 51 Pawlak (CR26) 2002; 147 Yang, Qin, Sang, Xu (CR37) 2021; 111 Zhang, Chen, Chen, Chen (CR17) 2018; 28 Hu, Yu, Xie (CR53) 2008; 34 Wei, Cui, Liang, Wang (CR15) 2016; 360 Liu, Yang, Fujita, Liu, Yang, Qian (CR58) 2019; 505 Ghaemi, Feizi-Derakhshi (CR24) 2016; 60 CR57 CR56 CR55 Zhang, Zhan, Wu (CR34) 2021; 29 Kanna, Santhi (CR42) 2021; 226 Hu, Pedrycz, Yu, Lang (CR54) 2009; 40 Zhang, Li, Liang (CR30) 2020; 507 Ding, Nayak, Naik, Pelusi, Mishara (CR1) 2021; 17 Baisantry, Sao, Shukla (CR10) 2022; 60 Tang, Zhang (CR7) 2022; 69 Sahlol, Elaziz, Al-Qaness, Kim (CR40) 2020; 8 Feng, Gong (CR39) 2022; 10 Guha, Ghosh, Bera, Sarkar, Mirjalili (CR18) 2023; 67 Shreem, Turabieh, Azwari, Baothman (CR23) 2022; 26 Zhang, Mao, Li, Tian (CR41) 2014; 281 Elaziz, Ouadfel, El-Latif, Ali Ibrahim (CR19) 2022; 14 Zhang, Mei, Li, Yang, Qian (CR3) 2023; 31 Yang, Yao (CR44) 2018; 70 Li, Kamnitsas, Glocker (CR8) 2021; 40 Luan, Li, Liu (CR21) 2016; 174 Chen, Li, Fan, Luo (CR49) 2019; 483 Jia, Rao, Shang, Li (CR51) 2020; 11 Wang, Deb, Cui (CR22) 2019; 31 Li, Liu (CR45) 2012; 24 Chen, Wang, Yang, Mi, Liu (CR50) 2021; 229 Yao (CR35) 1998; 111 CR61 CR60 An, Guo, Wang, Guo, Dai (CR36) 2023; 624 Momeni, Valdés, Rodrigues, Sandi, Atienza (CR12) 2022; 69 Penmatsa, Kalidindi, Mallidi (CR20) 2020; 14 Fujita, Gaeta, Loia, Orciuoli (CR29) 2020; 28 YJ Tang (5123_CR7) 2022; 69 N Momeni (5123_CR12) 2022; 69 RKV Penmatsa (5123_CR20) 2020; 14 C Zhang (5123_CR30) 2020; 507 J Qian (5123_CR31) 2023; 44 ZC Gong (5123_CR43) 2022; 13 QH Hu (5123_CR54) 2009; 40 HM Chen (5123_CR49) 2019; 483 5123_CR61 GG Wang (5123_CR22) 2019; 31 YY Yao (5123_CR35) 1998; 111 5123_CR60 WP Ding (5123_CR11) 2021; 5 Z Pawlak (5123_CR26) 2002; 147 V Aksakalli (5123_CR62) 2016; 75 QH Hu (5123_CR53) 2008; 34 YD Zhang (5123_CR41) 2014; 281 DC Li (5123_CR45) 2012; 24 JD Feng (5123_CR39) 2022; 10 L Yang (5123_CR37) 2021; 111 WW Yan (5123_CR13) 2022; 10 A Campagner (5123_CR25) 2021; 136 W Wei (5123_CR14) 2018; 140 Y Chen (5123_CR50) 2021; 229 XY Jia (5123_CR51) 2020; 11 5123_CR9 MA Tawhid (5123_CR27) 2020; 11 TH Xu (5123_CR28) 2020; 118 QH Hu (5123_CR33) 2008; 178 X Zhang (5123_CR3) 2023; 31 M Baisantry (5123_CR10) 2022; 60 Z Chen (5123_CR47) 2022; 140 YY Yao (5123_CR59) 2008; 5150 ZJ Li (5123_CR8) 2021; 40 R Guha (5123_CR18) 2023; 67 MA Elaziz (5123_CR19) 2022; 14 5123_CR48 QH Hu (5123_CR52) 2010; 51 5123_CR46 XY Luan (5123_CR21) 2016; 174 XB Yang (5123_CR32) 2019; 105 H Fujita (5123_CR29) 2020; 28 O Etesami (5123_CR16) 2020; 285 PR Kanna (5123_CR42) 2021; 226 K Zhang (5123_CR34) 2021; 29 JK Chen (5123_CR4) 2022; 30 WP Ding (5123_CR1) 2021; 17 5123_CR55 XB Yang (5123_CR44) 2018; 70 5123_CR57 S An (5123_CR36) 2023; 624 5123_CR56 AT Sahlol (5123_CR40) 2020; 8 KY Liu (5123_CR5) 2022; 131 W Wei (5123_CR15) 2016; 360 A Zhang (5123_CR17) 2018; 28 A Ismail (5123_CR6) 2022; 69 M Ghaemi (5123_CR24) 2016; 60 LJ Dong (5123_CR2) 2023; 467 L Zou (5123_CR38) 2019; 7 KY Liu (5123_CR58) 2019; 505 SS Shreem (5123_CR23) 2022; 26
References_xml	– volume: 229 year: 2021 ident: CR50 article-title: Granular ball guided selector for attribute reduction publication-title: Knowl Based Syst – volume: 5 start-page: 29 issue: 1 year: 2021 end-page: 41 ident: CR11 article-title: Coevolutionary fuzzy at tribute order reduction with complete attribute-value space tree publication-title: IEEE Trans Emerging Top Comput Intell – volume: 111 start-page: 239 year: 1998 end-page: 259 ident: CR35 article-title: Relational interpretations of neighborhood operators and rough set approximation operators publication-title: Inf Sci – volume: 69 start-page: 424 issue: 2 year: 2022 end-page: 428 ident: CR6 article-title: A low-complexity endurance modulation for flash memory publication-title: IEEE Trans Circ Syst II: Express Briefs – volume: 11 start-page: 573 issue: 3 year: 2020 end-page: 602 ident: CR27 article-title: Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm publication-title: Int J Mach Learn Cybern – volume: 360 start-page: 181 year: 2016 end-page: 201 ident: CR15 article-title: Fuzzy rough approximations for set-valued data publication-title: Inf Sci – volume: 31 start-page: 1995 issue: 7 year: 2019 end-page: 2014 ident: CR22 publication-title: Monarch butterfly optimization. Neural Comput Appl – volume: 31 start-page: 1981 issue: 6 year: 2023 end-page: 1994 ident: CR3 article-title: Instance and feature selection using fuzzy rough sets: a bi-selection approach for data reduction publication-title: IEEE Trans Fuzzy Syst – volume: 40 start-page: 137 issue: 1 year: 2009 end-page: 150 ident: CR54 article-title: Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization publication-title: IEEE Trans Syst Man Cybern B – volume: 13 start-page: 3645 issue: 11 year: 2022 end-page: 3662 ident: CR43 article-title: Unsupervised attribute reduction: improving effectiveness and efficiency publication-title: Int J Mach Learn Cybern – volume: 11 start-page: 1047 issue: 5 year: 2020 end-page: 1060 ident: CR51 article-title: Similarity-based attribute reduction in rough set theory: A clustering perspective publication-title: Int J Mach Learn Cybern – ident: CR61 – volume: 281 start-page: 586 year: 2014 end-page: 600 ident: CR41 article-title: Salient region detection for complex background images using integrated features publication-title: Inf Sci – volume: 111 year: 2021 ident: CR37 article-title: Dynamic fuzzy neighborhood rough set approach for interval-valued information systems with fuzzy decision publication-title: Appl Soft Comput – volume: 70 start-page: 1 year: 2018 end-page: 11 ident: CR44 article-title: Ensemble selector for attribute reduction publication-title: Appl Soft Comput – ident: CR46 – volume: 17 start-page: 4298 issue: 6 year: 2021 end-page: 4307 ident: CR1 article-title: Fuzzy and real-coded chemical reaction optimization for intrusion detection in industrial big data environment publication-title: IEEE Trans Ind Inf – volume: 40 start-page: 1065 issue: 3 year: 2021 end-page: 1077 ident: CR8 article-title: Analyzing overfitting under class imbalance in neural networks for image segmentation publication-title: IEEE Trans Med Imaging – volume: 34 start-page: 866 issue: 2 year: 2008 end-page: 876 ident: CR53 publication-title: Neighborhood classifiers. Expert Syst Appl – volume: 131 year: 2022 ident: CR5 article-title: Neighborhood rough set based ensemble feature selection with cross-class sample granulation publication-title: Appl Soft Comput – volume: 30 start-page: 2886 issue: 8 year: 2022 end-page: 2901 ident: CR4 article-title: A spectral feature selection approach with kernelized fuzzy rough sets publication-title: IEEE Trans Fuzzy Syst – volume: 136 start-page: 150 year: 2021 end-page: 167 ident: CR25 article-title: Rough set-based feature selection for weakly labeled data publication-title: Int J Approx Reason – volume: 140 start-page: 75 year: 2022 end-page: 91 ident: CR47 article-title: Random sampling accelerator for attribute reduction publication-title: Int J Approx Reason – ident: CR9 – volume: 28 start-page: 54 year: 2018 end-page: 62 ident: CR17 article-title: On the NP-hardness of scheduling with time restrictions publication-title: Discret Optim – ident: CR57 – ident: CR60 – volume: 69 start-page: 694 issue: 2 year: 2022 end-page: 706 ident: CR7 article-title: Low-complexity resource-shareable parallel generalized integrated interleaved encoder publication-title: IEEE Trans Circuits Syst I Regul Pap – volume: 69 start-page: 1072 issue: 3 year: 2022 end-page: 1084 ident: CR12 article-title: CAFS: Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices publication-title: IEEE Trans Biomed Eng – volume: 14 start-page: 95 issue: 3 year: 2020 end-page: 114 ident: CR20 article-title: Feature reduction and optimization of malware detection system using ant colony optimization and rough sets publication-title: Int J Inf Secur Priv – volume: 483 start-page: 1 year: 2019 end-page: 20 ident: CR49 article-title: Feature selection for imbalanced data based on neighborhood rough sets publication-title: Inf Sci – volume: 28 start-page: 831 issue: 5 year: 2020 end-page: 845 ident: CR29 article-title: Hypotheses analysis and assessment in counterterrorism activities: a method based on OWA and fuzzy probabilistic rough sets publication-title: IEEE Trans Fuzzy Syst – volume: 285 start-page: 526 year: 2020 end-page: 529 ident: CR16 article-title: On NP-hard graph properties characterized by the spectrum publication-title: Discret Appl Math – volume: 14 start-page: 2274 issue: 6 year: 2022 end-page: 2295 ident: CR19 article-title: Feature Selection Based on Modified Bio-inspired Atomic Orbital Search Using Arithmetic Optimization and Opposite-Based Learning publication-title: Cognit Comput – volume: 505 start-page: 457 year: 2019 end-page: 472 ident: CR58 article-title: An efficient selector for multi-granularity attribute reduction publication-title: Inf Sci – volume: 118 start-page: 64 year: 2020 end-page: 78 ident: CR28 article-title: Finding strongly connected components of simple digraphs based on granulation strategy publication-title: Int J Approx Reason – volume: 507 start-page: 665 year: 2020 end-page: 683 ident: CR30 article-title: Multi-granularity three-way decisions with adjustable hesitant fuzzy linguistic multigranulation decision-theoretic rough sets over two universes publication-title: Inf Sci – volume: 147 start-page: 1 issue: 1–4 year: 2002 end-page: 12 ident: CR26 article-title: Rough sets and intelligent data analysis publication-title: Inf Sci – volume: 178 start-page: 3577 issue: 18 year: 2008 end-page: 3594 ident: CR33 article-title: Neighborhood rough set based heterogeneous feature subset selection publication-title: Inf Sci – ident: CR56 – volume: 467 year: 2023 ident: CR2 article-title: Incremental feature selection with fuzzy rough sets for dynamic data sets publication-title: Fuzzy Sets Syst – volume: 67 start-page: 1877 year: 2023 end-page: 7503 ident: CR18 article-title: Discrete equilibrium optimizer combined with simulated annealing for feature selection publication-title: J Comput Sci – volume: 174 start-page: 522 year: 2016 end-page: 529 ident: CR21 article-title: A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm publication-title: Neurocomputing – ident: CR48 – volume: 7 start-page: 90277 year: 2019 end-page: 90288 ident: CR38 article-title: An Improved Fish Swarm Algorithm for Neighborhood Rough Set Reduction and its Application publication-title: IEEE Access – volume: 44 start-page: 5617 issue: 4 year: 2023 end-page: 5631 ident: CR31 article-title: Multi-granularity decision-theoretic rough sets based on the fuzzy T-equivalence relation with new strategies publication-title: J Intell Fuzzy Syst – volume: 10 start-page: 553 issue: 4 year: 2022 ident: CR13 article-title: Beam-Influenced Attribute Selector for Producing Stable Reduct publication-title: Mathematics – volume: 105 start-page: 112 year: 2019 end-page: 129 ident: CR32 article-title: Pseudo-label neighborhood rough set: Measures and attribute reductions publication-title: Int J Approx Reason – volume: 51 start-page: 453 issue: 4 year: 2010 end-page: 471 ident: CR52 article-title: Gaussian kernel based fuzzy rough sets: Model uncertainty measures and applications publication-title: Int J Approx Reason – volume: 226 year: 2021 ident: CR42 article-title: Unified Deep Learning approach for Efficient Intrusion Detection System using Integrated Spatial-Temporal Features publication-title: Knowl Based Syst – volume: 75 start-page: 41 year: 2016 end-page: 47 ident: CR62 article-title: Feature selection via binary simultaneous perturbation stochastic approximation publication-title: Pattern Recognit Lett – volume: 10 start-page: 33301 year: 2022 end-page: 33312 ident: CR39 article-title: A Novel Feature Selection Method With Neighborhood Rough Set and Improved Particle Swarm Optimization publication-title: IEEE Access – volume: 5150 start-page: 100 year: 2008 end-page: 117 ident: CR59 article-title: On reduct construction algorithms publication-title: Trans. Comput. Sci. II – volume: 60 start-page: 121 year: 2016 end-page: 129 ident: CR24 article-title: Feature selection using Forest Optimization Algorithm publication-title: Pattern Recognit – ident: CR55 – volume: 624 start-page: 185 year: 2023 end-page: 199 ident: CR36 article-title: A soft neighborhood rough set model and its applications publication-title: Inf Sci – volume: 60 start-page: 1 year: 2022 end-page: 14 ident: CR10 article-title: Discriminative spectral spatial feature extraction-based band selection for hyper spectral image classification publication-title: IEEE Trans Geosci Remote Sens – volume: 29 start-page: 2491 issue: 9 year: 2021 end-page: 2505 ident: CR34 article-title: On multi-criteria decision-making method based on a fuzzy rough set model with fuzzy -neighborhoods publication-title: IEEE Trans Fuzzy Syst – volume: 140 start-page: 142 year: 2018 end-page: 157 ident: CR14 article-title: Discernibility matrix based incremental attribute reduction for dynamic data publication-title: Knowl Based Syst – volume: 24 start-page: 452 issue: 3 year: 2012 end-page: 464 ident: CR45 article-title: Extending attribute information for small data set classification publication-title: IEEE Trans Knowl Data Eng – volume: 8 start-page: 23011 year: 2020 end-page: 23021 ident: CR40 article-title: Handwritten Arabic Optical Character Recognition Approach Based on Hybrid Whale Optimization Algorithm With Neighborhood Rough Set publication-title: IEEE Access – volume: 26 start-page: 1811 issue: 4 year: 2022 end-page: 1823 ident: CR23 article-title: Enhanced binary genetic algorithm as a feature selection to predict student performance publication-title: Soft Comput – volume: 75 start-page: 41 year: 2016 ident: 5123_CR62 publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2016.03.002 – volume: 140 start-page: 142 year: 2018 ident: 5123_CR14 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2017.10.033 – ident: 5123_CR46 – ident: 5123_CR57 doi: 10.1016/j.knosys.2020.106014 – volume: 31 start-page: 1981 issue: 6 year: 2023 ident: 5123_CR3 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2022.3216990 – volume: 118 start-page: 64 year: 2020 ident: 5123_CR28 publication-title: Int J Approx Reason doi: 10.1016/j.ijar.2019.12.001 – volume: 44 start-page: 5617 issue: 4 year: 2023 ident: 5123_CR31 publication-title: J Intell Fuzzy Syst doi: 10.3233/IFS-222910 – volume: 105 start-page: 112 year: 2019 ident: 5123_CR32 publication-title: Int J Approx Reason doi: 10.1016/j.ijar.2018.11.010 – volume: 14 start-page: 2274 issue: 6 year: 2022 ident: 5123_CR19 publication-title: Cognit Comput doi: 10.1007/s12559-022-10022-6 – volume: 11 start-page: 1047 issue: 5 year: 2020 ident: 5123_CR51 publication-title: Int J Mach Learn Cybern doi: 10.1007/s13042-019-00959-w – volume: 26 start-page: 1811 issue: 4 year: 2022 ident: 5123_CR23 publication-title: Soft Comput doi: 10.1007/s00500-021-06424-7 – volume: 28 start-page: 54 year: 2018 ident: 5123_CR17 publication-title: Discret Optim doi: 10.1016/j.disopt.2017.12.001 – volume: 34 start-page: 866 issue: 2 year: 2008 ident: 5123_CR53 publication-title: Neighborhood classifiers. Expert Syst Appl doi: 10.1016/j.eswa.2006.10.043 – volume: 5 start-page: 29 issue: 1 year: 2021 ident: 5123_CR11 publication-title: IEEE Trans Emerging Top Comput Intell doi: 10.1109/TETCI.2020.2998919 – volume: 40 start-page: 1065 issue: 3 year: 2021 ident: 5123_CR8 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2020.3046692 – volume: 69 start-page: 424 issue: 2 year: 2022 ident: 5123_CR6 publication-title: IEEE Trans Circ Syst II: Express Briefs – volume: 111 start-page: 239 year: 1998 ident: 5123_CR35 publication-title: Inf Sci doi: 10.1016/S0020-0255(98)10006-3 – ident: 5123_CR60 doi: 10.1609/aaai.v35i13.17362 – volume: 13 start-page: 3645 issue: 11 year: 2022 ident: 5123_CR43 publication-title: Int J Mach Learn Cybern doi: 10.1007/s13042-022-01618-3 – volume: 14 start-page: 95 issue: 3 year: 2020 ident: 5123_CR20 publication-title: Int J Inf Secur Priv doi: 10.4018/IJISP.2020070106 – ident: 5123_CR9 doi: 10.1109/TKDE.2019.2959988 – volume: 69 start-page: 1072 issue: 3 year: 2022 ident: 5123_CR12 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2021.3113593 – volume: 507 start-page: 665 year: 2020 ident: 5123_CR30 publication-title: Inf Sci doi: 10.1016/j.ins.2019.01.033 – ident: 5123_CR55 doi: 10.1109/TNNLS.2022.3184120 – volume: 40 start-page: 137 issue: 1 year: 2009 ident: 5123_CR54 publication-title: IEEE Trans Syst Man Cybern B – volume: 69 start-page: 694 issue: 2 year: 2022 ident: 5123_CR7 publication-title: IEEE Trans Circuits Syst I Regul Pap doi: 10.1109/TCSI.2021.3118301 – volume: 140 start-page: 75 year: 2022 ident: 5123_CR47 publication-title: Int J Approx Reason doi: 10.1016/j.ijar.2021.09.016 – volume: 10 start-page: 33301 year: 2022 ident: 5123_CR39 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3162074 – volume: 30 start-page: 2886 issue: 8 year: 2022 ident: 5123_CR4 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2021.3096212 – volume: 467 year: 2023 ident: 5123_CR2 publication-title: Fuzzy Sets Syst – volume: 31 start-page: 1995 issue: 7 year: 2019 ident: 5123_CR22 publication-title: Monarch butterfly optimization. Neural Comput Appl doi: 10.1007/s00521-015-1923-y – volume: 285 start-page: 526 year: 2020 ident: 5123_CR16 publication-title: Discret Appl Math doi: 10.1016/j.dam.2020.06.020 – volume: 483 start-page: 1 year: 2019 ident: 5123_CR49 publication-title: Inf Sci doi: 10.1016/j.ins.2019.01.041 – ident: 5123_CR56 doi: 10.1109/TFUZZ.2022.3217377 – volume: 70 start-page: 1 year: 2018 ident: 5123_CR44 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2018.05.013 – volume: 624 start-page: 185 year: 2023 ident: 5123_CR36 publication-title: Inf Sci doi: 10.1016/j.ins.2022.12.074 – volume: 111 year: 2021 ident: 5123_CR37 publication-title: Appl Soft Comput – volume: 67 start-page: 1877 year: 2023 ident: 5123_CR18 publication-title: J Comput Sci doi: 10.1016/j.jocs.2023.101942 – volume: 17 start-page: 4298 issue: 6 year: 2021 ident: 5123_CR1 publication-title: IEEE Trans Ind Inf doi: 10.1109/TII.2020.3007419 – ident: 5123_CR61 doi: 10.1109/CVPR.2019.00723 – ident: 5123_CR48 doi: 10.3390/sym14091828 – volume: 505 start-page: 457 year: 2019 ident: 5123_CR58 publication-title: Inf Sci doi: 10.1016/j.ins.2019.07.051 – volume: 5150 start-page: 100 year: 2008 ident: 5123_CR59 publication-title: Trans. Comput. Sci. II doi: 10.1007/978-3-540-87563-5_6 – volume: 24 start-page: 452 issue: 3 year: 2012 ident: 5123_CR45 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2010.254 – volume: 51 start-page: 453 issue: 4 year: 2010 ident: 5123_CR52 publication-title: Int J Approx Reason doi: 10.1016/j.ijar.2010.01.004 – volume: 29 start-page: 2491 issue: 9 year: 2021 ident: 5123_CR34 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2020.3001670 – volume: 11 start-page: 573 issue: 3 year: 2020 ident: 5123_CR27 publication-title: Int J Mach Learn Cybern doi: 10.1007/s13042-019-00996-5 – volume: 360 start-page: 181 year: 2016 ident: 5123_CR15 publication-title: Inf Sci doi: 10.1016/j.ins.2016.04.005 – volume: 10 start-page: 553 issue: 4 year: 2022 ident: 5123_CR13 publication-title: Mathematics doi: 10.3390/math10040553 – volume: 147 start-page: 1 issue: 1–4 year: 2002 ident: 5123_CR26 publication-title: Inf Sci doi: 10.1016/S0020-0255(02)00197-4 – volume: 7 start-page: 90277 year: 2019 ident: 5123_CR38 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2926799 – volume: 281 start-page: 586 year: 2014 ident: 5123_CR41 publication-title: Inf Sci doi: 10.1016/j.ins.2013.12.043 – volume: 8 start-page: 23011 year: 2020 ident: 5123_CR40 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2970438 – volume: 178 start-page: 3577 issue: 18 year: 2008 ident: 5123_CR33 publication-title: Inf Sci doi: 10.1016/j.ins.2008.05.024 – volume: 28 start-page: 831 issue: 5 year: 2020 ident: 5123_CR29 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2019.2955047 – volume: 60 start-page: 1 year: 2022 ident: 5123_CR10 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2021.3129841 – volume: 136 start-page: 150 year: 2021 ident: 5123_CR25 publication-title: Int J Approx Reason doi: 10.1016/j.ijar.2021.06.005 – volume: 174 start-page: 522 year: 2016 ident: 5123_CR21 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.090 – volume: 60 start-page: 121 year: 2016 ident: 5123_CR24 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2016.05.012 – volume: 226 year: 2021 ident: 5123_CR42 publication-title: Knowl Based Syst – volume: 131 year: 2022 ident: 5123_CR5 publication-title: Appl Soft Comput – volume: 229 year: 2021 ident: 5123_CR50 publication-title: Knowl Based Syst
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SubjectTerms	Algorithms Artificial Intelligence Classification Computer Science Datasets Feature selection Heuristic Heuristic methods Machines Manufacturing Mechanical Engineering Perturbation Processes Random sampling Stability
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Title	A meta-heuristic feature selection algorithm combining random sampling accelerator and ensemble using data perturbation
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