Feature selection based on dataset variance optimization using Hybrid Sine Cosine – Firehawk Algorithm (HSCFHA)
Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the pr...
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| Veröffentlicht in: | Future generation computer systems Jg. 155; S. 272 - 286 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.06.2024
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the predictive accuracy of a learning algorithm to form a condensed set of features. Traditionally, this method uses K-Nearest Neighbor (KNN) for maximizing accuracy as its cost function. However, this approach often yields less than optimal results in large sample spaces and demands considerable computational resources. To circumvent the shortcomings of this approach, this work proposes a novel metaheuristic algorithm, termed the Hybrid Sine Cosine Firehawk Algorithm. Furthermore, a novel feature selection technique is designed that uses this hybrid algorithm to eliminate insignificant and redundant features by incorporating the minimization of dataset variance in the cost function. Additionally, the hybridization of multiple metaheuristic algorithms produces the best features of each algorithm to improve the exploration ability. The proposed technique is tested on 22 University of California Irvine datasets containing low, medium and high dimensional datasets and compared to the traditional KNN-based approach. The technique is also compared with other state-of-the-art metaheuristic techniques, namely Particle Swarm Optimizer, Grey Wolf Optimizer, Whale Optimization Algorithm, Hybrid Ant Colony Optimizer and Improved Binary Bat Algorithm. The results show significant improvements over previous techniques in terms of minimal loss in essential data while reducing the size of the raw data in considerably less time, as well as a well-balanced confusion matrix.
•Feature Selection using Variance minimization.•Hybrid Sine Cosine - Firehawk Algorithm•Comparative Analysis with metaheuristic techniques.•Tested on multi-dimensional and bi/multi class datasets. |
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| AbstractList | Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the predictive accuracy of a learning algorithm to form a condensed set of features. Traditionally, this method uses K-Nearest Neighbor (KNN) for maximizing accuracy as its cost function. However, this approach often yields less than optimal results in large sample spaces and demands considerable computational resources. To circumvent the shortcomings of this approach, this work proposes a novel metaheuristic algorithm, termed the Hybrid Sine Cosine Firehawk Algorithm. Furthermore, a novel feature selection technique is designed that uses this hybrid algorithm to eliminate insignificant and redundant features by incorporating the minimization of dataset variance in the cost function. Additionally, the hybridization of multiple metaheuristic algorithms produces the best features of each algorithm to improve the exploration ability. The proposed technique is tested on 22 University of California Irvine datasets containing low, medium and high dimensional datasets and compared to the traditional KNN-based approach. The technique is also compared with other state-of-the-art metaheuristic techniques, namely Particle Swarm Optimizer, Grey Wolf Optimizer, Whale Optimization Algorithm, Hybrid Ant Colony Optimizer and Improved Binary Bat Algorithm. The results show significant improvements over previous techniques in terms of minimal loss in essential data while reducing the size of the raw data in considerably less time, as well as a well-balanced confusion matrix.
•Feature Selection using Variance minimization.•Hybrid Sine Cosine - Firehawk Algorithm•Comparative Analysis with metaheuristic techniques.•Tested on multi-dimensional and bi/multi class datasets. |
| Author | Li, Kai Ni, Wei Saadat, Ahsan Moosavi, Syed Kumayl Raza Guizani, Mohsen Abaid, Zainab |
| Author_xml | – sequence: 1 givenname: Syed Kumayl Raza orcidid: 0000-0001-7064-255X surname: Moosavi fullname: Moosavi, Syed Kumayl Raza organization: National University of Sciences and Technology (NUST), Islamabad, Pakistan – sequence: 2 givenname: Ahsan surname: Saadat fullname: Saadat, Ahsan email: ahsan.saadat@seecs.edu.pk organization: National University of Sciences and Technology (NUST), Islamabad, Pakistan – sequence: 3 givenname: Zainab surname: Abaid fullname: Abaid, Zainab organization: FAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan – sequence: 4 givenname: Wei orcidid: 0000-0003-0780-4637 surname: Ni fullname: Ni, Wei organization: Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia – sequence: 5 givenname: Kai surname: Li fullname: Li, Kai organization: CISTER Research Centre, Portugal – sequence: 6 givenname: Mohsen surname: Guizani fullname: Guizani, Mohsen organization: Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates |
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| Cites_doi | 10.1016/j.advengsoft.2013.12.007 10.1016/j.asoc.2016.01.044 10.1007/s00521-023-08772-x 10.1504/IJCAT.2013.056915 10.1016/j.ijepes.2016.03.007 10.1007/s00521-021-05997-6 10.1023/A:1010933404324 10.1093/bioinformatics/btm344 10.1007/s00530-015-0494-1 10.1109/ASIANCON51346.2021.9544105 10.1016/j.neucom.2014.06.006 10.1111/j.2517-6161.1996.tb02080.x 10.1016/S0004-3702(97)00043-X 10.1145/3059336.3059363 10.1002/int.22703 10.1016/j.knosys.2015.12.022 10.1016/j.patcog.2021.107933 10.1007/s00521-020-04761-6 10.1016/j.asoc.2016.08.011 10.1016/j.advengsoft.2016.01.008 10.1007/s12652-020-02623-6 10.1186/s12859-019-2754-0 10.1007/s00521-013-1525-5 10.1109/ACCESS.2020.3035531 10.3923/pjbs.2014.266.271 10.1007/s13369-017-2790-x 10.1016/j.eswa.2021.115290 10.1109/TPAMI.2013.50 10.1016/j.patcog.2021.108169 10.1016/j.fusengdes.2017.03.042 10.1109/TGRS.2019.2958812 10.1016/j.compeleceng.2013.11.024 10.1007/s10479-005-3971-7 10.1007/s10586-021-03459-1 10.1089/cmb.2015.0189 10.1007/s00521-017-2837-7 10.1126/science.1127647 |
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| Keywords | Hybrid Sine Cosine Firehawk Algorithm Feature selection Machine learning Classification Optimization algorithms Dataset variance optimization Metaheuristic algorithms |
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| References | Mafarja, Abdullah (b43) 2013; 48 Tibshirani (b18) 1996; 58 Sindhu (b36) 2017; 28 Neapolitan (b24) 2004 Gendreau, Potvin (b26) 2005; 140 Breiman (b19) 2001; 45 Alzubi (b31) 2022; 25 Yue (b47) 2019 Movassagh, Alzubi, Gheisari (b51) 2023; 14 Eiben, Smith (b3) 2015 T. Hastie (b13) 2009 Mirjalili (b55) 2014; 25 Chandrashekar, Sahin (b20) 2014; 40 Mirjalili, Lewis (b57) 2016; 95 Jr., Yang, Fister (b27) 2013 Wan (b40) 2020; 58 Moosavi, Zafar, Akhter (b49) 2021 Stephan, Stephan, Kannan (b7) 2021; 33 Kalyani, Chaudhari (b10) 2022; 34 Mirjalili, Mirjalili, Lewis (b53) 2014; 69 Hijazi (b5) 2021; 182 Tuba, Bacanin (b11) 2014; 143 Alawad, Abed-alguni, Al-Betar (b58) 2023; 35 Jona, Nagaveni (b33) 2014; 17 Kohavi, John (b14) 1997; 97 L. Yu, H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, in: Proceedings of the 20th International Conference on Machine Learning, ICML-03, 2003, pp. 856–863. Dey, Chattopadhyay (b41) 2020; 8 Hinton, Salakhutdinov (b22) 2006; 313 Gao, Song, Liu, Shao (b23) 2017; 23 Sangaiah, Zhang, Sheng (b28) 2018 Mirjalili (b45) 2016; 96 (b52) 2023 Zafar, Khan, Moosavi (b48) 2022 Moosavi, Younis, Zafar (b50) 2022 R. Kommadath, J. Dondeti, P. Kotecha, Benchmarking Jaya and sine cosine algorithm on real parameter bound constrained single objective optimization problems, in: Proceedings of the 2017 International Conference on Intelligent Systems, cec2016, 2017, pp. 31–34. Wan, Wang, Ye, Lai (b32) 2016; 49 Alzubi, Alzubi, Tedmori, Rashaideh, Almomani (b9) 2018; 15 Hasanin (b21) 2019 Taghian, Nadimi-Shahraki (b38) 2019 Li, Chen, Wasserman (b16) 2016; 23 Moradi, Gholampour (b34) 2016; 43 Saeys, Inza, Larranaga (b4) 2007; 23 Yu, Aouari, Mansour (b6) 2021; 3 Zhao, Jiang (b2) 2005; vol. 293 Masoudi-Sobhanzadeh (b15) 2019; 20 Kennedy, Eberhart (b54) 1995; vol. 4 S. Pathak, et al., A New Salp Swarm Algorithm for the Numerical Optimization Problems Based on An Elite Opposition-based Learning, in: 2021 Asian Conference on Innovation in Technology, ASIANCON, 2021, pp. 1–6. Bengio (b17) 2013; 35 Madhura, Mahalakhsmi (b1) 2022; 100 Azizi, Talatahari, Gandomi (b44) 2022 Pereira, Rodrigues (b29) 2014 Alzubi, Alzubi, Alweshah, Qiqieh, Al-Shami, Manikandan (b8) 2020; 32 Fan, Liu, Liu, Du (b25) 2021; 120 Chattopadhyay (b37) 2022; 37 Reddy (b59) 2018; 43 Mao, Xie, Wang, Handroos (b35) 2017; 124 Belazzoug (b39) 2020; 32 Wen, Yin, Guo (b30) 2008; vol. 2 Lenin, Reddy, Suryakalavathi (b42) 2016; 82 Ma (b56) 2021; 116 Belazzoug (10.1016/j.future.2024.02.017_b39) 2020; 32 Mafarja (10.1016/j.future.2024.02.017_b43) 2013; 48 Wen (10.1016/j.future.2024.02.017_b30) 2008; vol. 2 Li (10.1016/j.future.2024.02.017_b16) 2016; 23 Azizi (10.1016/j.future.2024.02.017_b44) 2022 Moradi (10.1016/j.future.2024.02.017_b34) 2016; 43 Movassagh (10.1016/j.future.2024.02.017_b51) 2023; 14 Alzubi (10.1016/j.future.2024.02.017_b9) 2018; 15 Mao (10.1016/j.future.2024.02.017_b35) 2017; 124 Hinton (10.1016/j.future.2024.02.017_b22) 2006; 313 Tibshirani (10.1016/j.future.2024.02.017_b18) 1996; 58 Dey (10.1016/j.future.2024.02.017_b41) 2020; 8 Zafar (10.1016/j.future.2024.02.017_b48) 2022 Hasanin (10.1016/j.future.2024.02.017_b21) 2019 Masoudi-Sobhanzadeh (10.1016/j.future.2024.02.017_b15) 2019; 20 Mirjalili (10.1016/j.future.2024.02.017_b57) 2016; 95 Sindhu (10.1016/j.future.2024.02.017_b36) 2017; 28 Kennedy (10.1016/j.future.2024.02.017_b54) 1995; vol. 4 Mirjalili (10.1016/j.future.2024.02.017_b55) 2014; 25 Kalyani (10.1016/j.future.2024.02.017_b10) 2022; 34 Stephan (10.1016/j.future.2024.02.017_b7) 2021; 33 Reddy (10.1016/j.future.2024.02.017_b59) 2018; 43 Gao (10.1016/j.future.2024.02.017_b23) 2017; 23 Chattopadhyay (10.1016/j.future.2024.02.017_b37) 2022; 37 Alzubi (10.1016/j.future.2024.02.017_b31) 2022; 25 Mirjalili (10.1016/j.future.2024.02.017_b45) 2016; 96 Wan (10.1016/j.future.2024.02.017_b40) 2020; 58 Ma (10.1016/j.future.2024.02.017_b56) 2021; 116 Kohavi (10.1016/j.future.2024.02.017_b14) 1997; 97 10.1016/j.future.2024.02.017_b46 Zhao (10.1016/j.future.2024.02.017_b2) 2005; vol. 293 Chandrashekar (10.1016/j.future.2024.02.017_b20) 2014; 40 Yu (10.1016/j.future.2024.02.017_b6) 2021; 3 Neapolitan (10.1016/j.future.2024.02.017_b24) 2004 Alawad (10.1016/j.future.2024.02.017_b58) 2023; 35 T. Hastie (10.1016/j.future.2024.02.017_b13) 2009 Moosavi (10.1016/j.future.2024.02.017_b50) 2022 10.1016/j.future.2024.02.017_b12 Fan (10.1016/j.future.2024.02.017_b25) 2021; 120 Alzubi (10.1016/j.future.2024.02.017_b8) 2020; 32 Wan (10.1016/j.future.2024.02.017_b32) 2016; 49 Tuba (10.1016/j.future.2024.02.017_b11) 2014; 143 Gendreau (10.1016/j.future.2024.02.017_b26) 2005; 140 Jr. (10.1016/j.future.2024.02.017_b27) 2013 Saeys (10.1016/j.future.2024.02.017_b4) 2007; 23 10.1016/j.future.2024.02.017_b60 Mirjalili (10.1016/j.future.2024.02.017_b53) 2014; 69 Sangaiah (10.1016/j.future.2024.02.017_b28) 2018 Pereira (10.1016/j.future.2024.02.017_b29) 2014 Jona (10.1016/j.future.2024.02.017_b33) 2014; 17 Hijazi (10.1016/j.future.2024.02.017_b5) 2021; 182 Yue (10.1016/j.future.2024.02.017_b47) 2019 Breiman (10.1016/j.future.2024.02.017_b19) 2001; 45 Eiben (10.1016/j.future.2024.02.017_b3) 2015 (10.1016/j.future.2024.02.017_b52) 2023 Madhura (10.1016/j.future.2024.02.017_b1) 2022; 100 Taghian (10.1016/j.future.2024.02.017_b38) 2019 Bengio (10.1016/j.future.2024.02.017_b17) 2013; 35 Lenin (10.1016/j.future.2024.02.017_b42) 2016; 82 Moosavi (10.1016/j.future.2024.02.017_b49) 2021 |
| References_xml | – volume: vol. 4 start-page: 1942 year: 1995 end-page: 1948 ident: b54 article-title: Particle swarm optimization publication-title: Proceedings of ICNN’95-International Conference on Neural Networks – volume: 82 start-page: 87 year: 2016 end-page: 91 ident: b42 article-title: Hybrid Tabu search-simulated annealing method to solve optimal reactive power problem publication-title: Int. J. Electr. Power Energy Syst. – start-page: 1 year: 2022 end-page: 77 ident: b44 article-title: Fire Hawk optimizer: A novel metaheuristic algorithm publication-title: Artif. Intell. Rev. – start-page: 58 year: 2022 end-page: 70 ident: b50 article-title: A novel group teaching optimization algorithm based artificial neural network for classification publication-title: Intelligent Technologies and Applications: 4th International Conference – year: 2019 ident: b38 article-title: Binary sine cosine algorithms for feature selection from medical data – volume: 32 start-page: 454 year: 2020 end-page: 464 ident: b39 article-title: An improved sine cosine algorithm to select features for text categorization publication-title: J. King Saud Univ.-Comput. Inf. Sci. – year: 2019 ident: b47 article-title: Problem Definitions and Evaluation Criteria for the CEC 2020 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b57 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. – volume: 33 start-page: 13667 year: 2021 end-page: 13691 ident: b7 article-title: A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis publication-title: Neural Comput. Appl. – volume: 20 start-page: 1 year: 2019 end-page: 17 ident: b15 article-title: FeatureSelect: A software for feature selection based on machine learning approaches publication-title: BMC Bioinform. – volume: 23 start-page: 322 year: 2016 end-page: 336 ident: b16 article-title: Deep feature selection: Theory and application to identify enhancers and promoters publication-title: J. Comput. Biol. – year: 2004 ident: b24 publication-title: Learning Bayesian Networks – volume: 28 start-page: 2947 year: 2017 end-page: 2958 ident: b36 article-title: Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism publication-title: Neural Comput. Appl. – volume: vol. 293 start-page: 175 year: 2005 end-page: 182 ident: b2 article-title: Data mining for fault diagnosis and machine learning for rotating machinery publication-title: Key Engineering Materials – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: b22 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 25 start-page: 2369 year: 2022 end-page: 2387 ident: b31 article-title: An efficient malware detection approach with feature weighting based on Harris Hawks optimization publication-title: Cluster Comput. – start-page: 197 year: 2022 end-page: 209 ident: b48 article-title: Artificial Neural Network (ANN) trained by a novel arithmetic optimization algorithm (AOA) for short term forecasting of wind power publication-title: Intelligent Technologies and Applications: 4th International Conference – volume: 182 year: 2021 ident: b5 article-title: A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures publication-title: Expert Syst. Appl. – volume: 25 start-page: 663 year: 2014 end-page: 681 ident: b55 article-title: Binary bat algorithm publication-title: Neural Comput. Appl. – year: 2013 ident: b27 article-title: A brief review of nature-inspired algorithms for optimization – volume: 48 start-page: 195 year: 2013 end-page: 202 ident: b43 article-title: Investigating memetic algorithm in solving rough set attribute reduction publication-title: Int. J. Comput. Appl. Technol. – year: 2015 ident: b3 article-title: Introduction to Evolutionary Computing – volume: 32 start-page: 1 year: 2020 end-page: 17 ident: b8 article-title: An optimal pruning algorithm of classifier ensembles: dynamic programming approach publication-title: Neural Comput. Appl. – reference: L. Yu, H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, in: Proceedings of the 20th International Conference on Machine Learning, ICML-03, 2003, pp. 856–863. – volume: 116 year: 2021 ident: b56 article-title: A two-stage hybrid ant colony optimization for high-dimensional feature selection publication-title: Pattern Recognit. – volume: 17 start-page: 266 year: 2014 end-page: 271 ident: b33 article-title: Ant-cuckoo colony optimization for feature selection in digital mammogram publication-title: Pakistan J. Biol. Sci.: PJBS – volume: 43 start-page: 117 year: 2016 end-page: 130 ident: b34 article-title: A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy publication-title: Appl. Soft Comput. – volume: 49 start-page: 248 year: 2016 end-page: 258 ident: b32 article-title: A feature selection method based on modified binary coded ant colony optimization algorithm publication-title: Appl. Soft Comput. – volume: 58 start-page: 267 year: 1996 end-page: 288 ident: b18 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. – volume: 97 start-page: 273 year: 1997 end-page: 324 ident: b14 article-title: Wrappers for feature subset selection publication-title: Artif. Intell. – volume: 34 start-page: 2062 year: 2022 end-page: 2071 ident: b10 article-title: Data privacy preservation in MAC aware internet of things with optimized key generation publication-title: J. King Saud Univ.-Comput. and Inf. Sci. – start-page: 1 year: 2021 end-page: 6 ident: b49 article-title: A novel Artificial Neural Network (ANN) using the mayfly algorithm for classification publication-title: 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) – volume: 14 start-page: 6017 year: 2023 end-page: 6025 ident: b51 article-title: Artificial Neural Networks training algorithm integrating invasive weed optimization with differential evolutionary model publication-title: J. Ambient Intell. Humaniz. Comput. – volume: 3 start-page: 117 year: 2021 ident: b6 article-title: A hybrid algorithm based on PSO and GA for feature selection publication-title: J. Cybersecurity – volume: 40 start-page: 16 year: 2014 end-page: 28 ident: b20 article-title: A survey on feature selection methods publication-title: Comput. Electr. Eng. – reference: S. Pathak, et al., A New Salp Swarm Algorithm for the Numerical Optimization Problems Based on An Elite Opposition-based Learning, in: 2021 Asian Conference on Innovation in Technology, ASIANCON, 2021, pp. 1–6. – year: 2009 ident: b13 publication-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction – volume: 23 start-page: 2507 year: 2007 end-page: 2517 ident: b4 article-title: A review of feature selection techniques in bioinformatics publication-title: Bioinformatics – volume: 143 start-page: 197 year: 2014 end-page: 207 ident: b11 article-title: Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems publication-title: Neurocomputing – volume: 8 start-page: 200953 year: 2020 end-page: 200970 ident: b41 article-title: A hybrid meta-heuristic feature selection method using golden ratio and equilibrium optimization algorithms for speech emotion recognition publication-title: IEEE Access – volume: 35 start-page: 19427 year: 2023 end-page: 19451 ident: b58 article-title: Binary improved white shark algorithm for intrusion detection systems publication-title: Neural Comput. Appl. – volume: 35 start-page: 1798 year: 2013 end-page: 1828 ident: b17 article-title: Representation learning: A review and new perspectives publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: vol. 2 start-page: II year: 2008 end-page: 923 ident: b30 article-title: Ant colony optimization algorithm for feature selection and classification of multispectral remote sensing image publication-title: IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: b45 article-title: SCA: A sine cosine algorithm for solving optimization problems publication-title: Knowl.-Based Syst. – reference: R. Kommadath, J. Dondeti, P. Kotecha, Benchmarking Jaya and sine cosine algorithm on real parameter bound constrained single objective optimization problems, in: Proceedings of the 2017 International Conference on Intelligent Systems, cec2016, 2017, pp. 31–34. – volume: 15 start-page: 76 year: 2018 end-page: 86 ident: b9 article-title: Consensus-based combining method for classifier ensembles publication-title: Int. Arab J. Inf. Technol. – year: 2018 ident: b28 article-title: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications – year: 2023 ident: b52 article-title: UCI archives – volume: 140 start-page: 189 year: 2005 end-page: 213 ident: b26 article-title: Metaheuristics in combinatorial optimization publication-title: Ann. Oper. Res. – volume: 23 start-page: 303 year: 2017 end-page: 313 ident: b23 article-title: Learning in high-dimensional multimedia data: The state of the art publication-title: Multimedia Syst. – volume: 124 start-page: 587 year: 2017 end-page: 590 ident: b35 article-title: A hybrid differential evolution and particle swarm optimization algorithm for numerical kinematics solution of remote maintenance manipulators publication-title: Fusion Eng. Des. – volume: 58 start-page: 3601 year: 2020 end-page: 3618 ident: b40 article-title: Multiobjective hyperspectral feature selection based on discrete sine cosine algorithm publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b53 article-title: Grey Wolf Optimizer publication-title: Adv. Eng. Softw. – volume: 100 year: 2022 ident: b1 article-title: End2end unstructured data processing, confidential data structuring & storage using image processing, nlp, machine learning, and blockchain publication-title: J. Theoret. Appl. Inf. Technol. – volume: 120 year: 2021 ident: b25 article-title: Manifold learning with structured subspace for multi-label feature selection publication-title: Pattern Recognit. – volume: 37 start-page: 3777 year: 2022 end-page: 3814 ident: b37 article-title: Pneumonia detection from lung X-ray images using local search aided sine cosine algorithm based deep feature selection method publication-title: Int. J. Intell. Syst. – start-page: 346 year: 2019 end-page: 356 ident: b21 article-title: Investigating random undersampling and feature selection on bioinformatics big data publication-title: 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications – start-page: 141 year: 2014 end-page: 154 ident: b29 article-title: A binary cuckoo search and its application for feature selection publication-title: Cuckoo Search and Firefly Algorithm: Theory and Applications – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b19 article-title: Random forests publication-title: Mach. Learn. – volume: 43 start-page: 4041 year: 2018 end-page: 4056 ident: b59 article-title: A new binary variant of Sine–cosine algorithm: Development and application to solve profit-based unit commitment problem publication-title: Arab. J. Sci. Eng. – year: 2015 ident: 10.1016/j.future.2024.02.017_b3 – volume: vol. 4 start-page: 1942 year: 1995 ident: 10.1016/j.future.2024.02.017_b54 article-title: Particle swarm optimization – year: 2013 ident: 10.1016/j.future.2024.02.017_b27 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.future.2024.02.017_b53 article-title: Grey Wolf Optimizer publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.12.007 – volume: 43 start-page: 117 year: 2016 ident: 10.1016/j.future.2024.02.017_b34 article-title: A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.01.044 – volume: 15 start-page: 76 issue: 1 year: 2018 ident: 10.1016/j.future.2024.02.017_b9 article-title: Consensus-based combining method for classifier ensembles publication-title: Int. Arab J. Inf. Technol. – start-page: 1 year: 2022 ident: 10.1016/j.future.2024.02.017_b44 article-title: Fire Hawk optimizer: A novel metaheuristic algorithm publication-title: Artif. Intell. Rev. – volume: 35 start-page: 19427 year: 2023 ident: 10.1016/j.future.2024.02.017_b58 article-title: Binary improved white shark algorithm for intrusion detection systems publication-title: Neural Comput. Appl. doi: 10.1007/s00521-023-08772-x – start-page: 141 year: 2014 ident: 10.1016/j.future.2024.02.017_b29 article-title: A binary cuckoo search and its application for feature selection – volume: 48 start-page: 195 issue: 3 year: 2013 ident: 10.1016/j.future.2024.02.017_b43 article-title: Investigating memetic algorithm in solving rough set attribute reduction publication-title: Int. J. Comput. Appl. Technol. doi: 10.1504/IJCAT.2013.056915 – volume: 82 start-page: 87 year: 2016 ident: 10.1016/j.future.2024.02.017_b42 article-title: Hybrid Tabu search-simulated annealing method to solve optimal reactive power problem publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2016.03.007 – volume: 33 start-page: 13667 issue: 20 year: 2021 ident: 10.1016/j.future.2024.02.017_b7 article-title: A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis publication-title: Neural Comput. Appl. doi: 10.1007/s00521-021-05997-6 – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.future.2024.02.017_b19 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 23 start-page: 2507 issue: 19 year: 2007 ident: 10.1016/j.future.2024.02.017_b4 article-title: A review of feature selection techniques in bioinformatics publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm344 – volume: 23 start-page: 303 year: 2017 ident: 10.1016/j.future.2024.02.017_b23 article-title: Learning in high-dimensional multimedia data: The state of the art publication-title: Multimedia Syst. doi: 10.1007/s00530-015-0494-1 – ident: 10.1016/j.future.2024.02.017_b60 doi: 10.1109/ASIANCON51346.2021.9544105 – volume: 143 start-page: 197 year: 2014 ident: 10.1016/j.future.2024.02.017_b11 article-title: Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.06.006 – volume: 58 start-page: 267 issue: 1 year: 1996 ident: 10.1016/j.future.2024.02.017_b18 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 97 start-page: 273 issue: 1–2 year: 1997 ident: 10.1016/j.future.2024.02.017_b14 article-title: Wrappers for feature subset selection publication-title: Artif. Intell. doi: 10.1016/S0004-3702(97)00043-X – ident: 10.1016/j.future.2024.02.017_b46 doi: 10.1145/3059336.3059363 – volume: 37 start-page: 3777 issue: 7 year: 2022 ident: 10.1016/j.future.2024.02.017_b37 article-title: Pneumonia detection from lung X-ray images using local search aided sine cosine algorithm based deep feature selection method publication-title: Int. J. Intell. Syst. doi: 10.1002/int.22703 – volume: 32 start-page: 454 issue: 4 year: 2020 ident: 10.1016/j.future.2024.02.017_b39 article-title: An improved sine cosine algorithm to select features for text categorization publication-title: J. King Saud Univ.-Comput. Inf. Sci. – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.future.2024.02.017_b45 article-title: SCA: A sine cosine algorithm for solving optimization problems publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2015.12.022 – volume: 116 year: 2021 ident: 10.1016/j.future.2024.02.017_b56 article-title: A two-stage hybrid ant colony optimization for high-dimensional feature selection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2021.107933 – volume: 32 start-page: 1 year: 2020 ident: 10.1016/j.future.2024.02.017_b8 article-title: An optimal pruning algorithm of classifier ensembles: dynamic programming approach publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-04761-6 – volume: 49 start-page: 248 year: 2016 ident: 10.1016/j.future.2024.02.017_b32 article-title: A feature selection method based on modified binary coded ant colony optimization algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.08.011 – year: 2023 ident: 10.1016/j.future.2024.02.017_b52 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.future.2024.02.017_b57 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 14 start-page: 6017 year: 2023 ident: 10.1016/j.future.2024.02.017_b51 article-title: Artificial Neural Networks training algorithm integrating invasive weed optimization with differential evolutionary model publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-020-02623-6 – year: 2018 ident: 10.1016/j.future.2024.02.017_b28 – volume: 20 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.future.2024.02.017_b15 article-title: FeatureSelect: A software for feature selection based on machine learning approaches publication-title: BMC Bioinform. doi: 10.1186/s12859-019-2754-0 – volume: 25 start-page: 663 year: 2014 ident: 10.1016/j.future.2024.02.017_b55 article-title: Binary bat algorithm publication-title: Neural Comput. Appl. doi: 10.1007/s00521-013-1525-5 – year: 2019 ident: 10.1016/j.future.2024.02.017_b47 – start-page: 58 year: 2022 ident: 10.1016/j.future.2024.02.017_b50 article-title: A novel group teaching optimization algorithm based artificial neural network for classification – ident: 10.1016/j.future.2024.02.017_b12 – volume: 8 start-page: 200953 year: 2020 ident: 10.1016/j.future.2024.02.017_b41 article-title: A hybrid meta-heuristic feature selection method using golden ratio and equilibrium optimization algorithms for speech emotion recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3035531 – start-page: 197 year: 2022 ident: 10.1016/j.future.2024.02.017_b48 article-title: Artificial Neural Network (ANN) trained by a novel arithmetic optimization algorithm (AOA) for short term forecasting of wind power – volume: 17 start-page: 266 issue: 2 year: 2014 ident: 10.1016/j.future.2024.02.017_b33 article-title: Ant-cuckoo colony optimization for feature selection in digital mammogram publication-title: Pakistan J. Biol. Sci.: PJBS doi: 10.3923/pjbs.2014.266.271 – volume: 43 start-page: 4041 year: 2018 ident: 10.1016/j.future.2024.02.017_b59 article-title: A new binary variant of Sine–cosine algorithm: Development and application to solve profit-based unit commitment problem publication-title: Arab. J. Sci. Eng. doi: 10.1007/s13369-017-2790-x – volume: 34 start-page: 2062 issue: 5 year: 2022 ident: 10.1016/j.future.2024.02.017_b10 article-title: Data privacy preservation in MAC aware internet of things with optimized key generation publication-title: J. King Saud Univ.-Comput. and Inf. Sci. – volume: 182 year: 2021 ident: 10.1016/j.future.2024.02.017_b5 article-title: A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115290 – volume: 35 start-page: 1798 issue: 8 year: 2013 ident: 10.1016/j.future.2024.02.017_b17 article-title: Representation learning: A review and new perspectives publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2013.50 – volume: vol. 2 start-page: II year: 2008 ident: 10.1016/j.future.2024.02.017_b30 article-title: Ant colony optimization algorithm for feature selection and classification of multispectral remote sensing image – volume: 120 year: 2021 ident: 10.1016/j.future.2024.02.017_b25 article-title: Manifold learning with structured subspace for multi-label feature selection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2021.108169 – year: 2009 ident: 10.1016/j.future.2024.02.017_b13 – volume: 124 start-page: 587 year: 2017 ident: 10.1016/j.future.2024.02.017_b35 article-title: A hybrid differential evolution and particle swarm optimization algorithm for numerical kinematics solution of remote maintenance manipulators publication-title: Fusion Eng. Des. doi: 10.1016/j.fusengdes.2017.03.042 – start-page: 346 year: 2019 ident: 10.1016/j.future.2024.02.017_b21 article-title: Investigating random undersampling and feature selection on bioinformatics big data – volume: 58 start-page: 3601 issue: 5 year: 2020 ident: 10.1016/j.future.2024.02.017_b40 article-title: Multiobjective hyperspectral feature selection based on discrete sine cosine algorithm publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2958812 – volume: 40 start-page: 16 issue: 1 year: 2014 ident: 10.1016/j.future.2024.02.017_b20 article-title: A survey on feature selection methods publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2013.11.024 – volume: 140 start-page: 189 year: 2005 ident: 10.1016/j.future.2024.02.017_b26 article-title: Metaheuristics in combinatorial optimization publication-title: Ann. Oper. Res. doi: 10.1007/s10479-005-3971-7 – year: 2004 ident: 10.1016/j.future.2024.02.017_b24 – volume: 25 start-page: 2369 year: 2022 ident: 10.1016/j.future.2024.02.017_b31 article-title: An efficient malware detection approach with feature weighting based on Harris Hawks optimization publication-title: Cluster Comput. doi: 10.1007/s10586-021-03459-1 – year: 2019 ident: 10.1016/j.future.2024.02.017_b38 – volume: vol. 293 start-page: 175 year: 2005 ident: 10.1016/j.future.2024.02.017_b2 article-title: Data mining for fault diagnosis and machine learning for rotating machinery – volume: 23 start-page: 322 issue: 5 year: 2016 ident: 10.1016/j.future.2024.02.017_b16 article-title: Deep feature selection: Theory and application to identify enhancers and promoters publication-title: J. Comput. Biol. doi: 10.1089/cmb.2015.0189 – volume: 3 start-page: 117 issue: 2 year: 2021 ident: 10.1016/j.future.2024.02.017_b6 article-title: A hybrid algorithm based on PSO and GA for feature selection publication-title: J. Cybersecurity – start-page: 1 year: 2021 ident: 10.1016/j.future.2024.02.017_b49 article-title: A novel Artificial Neural Network (ANN) using the mayfly algorithm for classification – volume: 28 start-page: 2947 year: 2017 ident: 10.1016/j.future.2024.02.017_b36 article-title: Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-2837-7 – volume: 100 issue: 13 year: 2022 ident: 10.1016/j.future.2024.02.017_b1 article-title: End2end unstructured data processing, confidential data structuring & storage using image processing, nlp, machine learning, and blockchain publication-title: J. Theoret. Appl. Inf. Technol. – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.future.2024.02.017_b22 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 |
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