Multi-objective feature selection algorithm using Beluga Whale Optimization
The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in...
Uloženo v:
| Vydáno v: | Chemometrics and intelligent laboratory systems Ročník 257; s. 105295 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
15.02.2025
|
| Témata: | |
| ISSN: | 0169-7439 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in feature selection because it allows simultaneous evaluation of multiple, often conflicting objectives, such as maximizing model accuracy and minimizing the number of features. Traditional single-objective methods might focus solely on accuracy, often leading to models that are complex and computationally expensive. Multi-objective optimization, on the other hand, considers trade-offs between different criteria, identifying a set of optimal solutions (a Pareto front) where no one solution is clearly superior. It is especially useful when analyzing high-dimensional datasets, as it reduces overfitting and enhances model performance by selecting the most informative subset of features. This article introduces and evaluates the performance of the Binary version of Beluga Whale Optimization and the Multi-Objective Beluga Whale Optimization (MOBWO) algorithm in the context of feature selection. Features are encoded as binary matrices to denote their presence or absence, making it easier to stratify datasets. MOBWO emulates the exploration and exploitation patterns of Beluga Whale Optimization (BWO) through continuous search space. Optimal classification accuracy and minimum feature subset size are two conflicting objectives. The MOBWO was compared using 12 datasets from the University of California Irvine (UCI) repository with eleven well-known optimization algorithms, such as Genetic Algorithm (GA), Sine Cosine Algorithm (SCA), Bat Optimization Algorithm (BOA), Differential Evolution (DE), Whale Optimization Algorithm (WOA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Grasshopper Optimization Algorithm (MOGOA), Multi-Objective Non-dominated advanced Butterfly Optimization Algorithm (MONSBOA), and Multi-Objective Slime Mould Algorithm (MOSMA). In experiments using Random Forest (RF) as the classifier, different performance metrics were evaluated. The computational results show that the proposed BBWO algorithm achieves an average accuracy rate of 99.06 % across 12 datasets. Additionally, the proposed MOBWO algorithm outperforms existing multi-objective feature selection methods on all 12 datasets based on three metrics: Success Counting (SCC), Inverted Generational Distance (IGD), and Hypervolume indicators (HV). For instance, MOBWO achieves an average HV that is at least 3.54 % higher than all other methods.
•A binary variant of the Beluga Whale Optimization algorithm is developed.•The multi-objective version of BWO is proposed based on BWO principles.•Multiple conflicting objectives is addressed to solve feature selection problem.•The comprehensive evaluation ensures the robustness of the proposed method. |
|---|---|
| AbstractList | The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in feature selection because it allows simultaneous evaluation of multiple, often conflicting objectives, such as maximizing model accuracy and minimizing the number of features. Traditional single-objective methods might focus solely on accuracy, often leading to models that are complex and computationally expensive. Multi-objective optimization, on the other hand, considers trade-offs between different criteria, identifying a set of optimal solutions (a Pareto front) where no one solution is clearly superior. It is especially useful when analyzing high-dimensional datasets, as it reduces overfitting and enhances model performance by selecting the most informative subset of features. This article introduces and evaluates the performance of the Binary version of Beluga Whale Optimization and the Multi-Objective Beluga Whale Optimization (MOBWO) algorithm in the context of feature selection. Features are encoded as binary matrices to denote their presence or absence, making it easier to stratify datasets. MOBWO emulates the exploration and exploitation patterns of Beluga Whale Optimization (BWO) through continuous search space. Optimal classification accuracy and minimum feature subset size are two conflicting objectives. The MOBWO was compared using 12 datasets from the University of California Irvine (UCI) repository with eleven well-known optimization algorithms, such as Genetic Algorithm (GA), Sine Cosine Algorithm (SCA), Bat Optimization Algorithm (BOA), Differential Evolution (DE), Whale Optimization Algorithm (WOA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Grasshopper Optimization Algorithm (MOGOA), Multi-Objective Non-dominated advanced Butterfly Optimization Algorithm (MONSBOA), and Multi-Objective Slime Mould Algorithm (MOSMA). In experiments using Random Forest (RF) as the classifier, different performance metrics were evaluated. The computational results show that the proposed BBWO algorithm achieves an average accuracy rate of 99.06 % across 12 datasets. Additionally, the proposed MOBWO algorithm outperforms existing multi-objective feature selection methods on all 12 datasets based on three metrics: Success Counting (SCC), Inverted Generational Distance (IGD), and Hypervolume indicators (HV). For instance, MOBWO achieves an average HV that is at least 3.54 % higher than all other methods.
•A binary variant of the Beluga Whale Optimization algorithm is developed.•The multi-objective version of BWO is proposed based on BWO principles.•Multiple conflicting objectives is addressed to solve feature selection problem.•The comprehensive evaluation ensures the robustness of the proposed method. |
| ArticleNumber | 105295 |
| Author | Mohammad Hasani Zade, Behnam Mansouri, Najme Kouhpah Esfahani, Kiana |
| Author_xml | – sequence: 1 givenname: Kiana surname: Kouhpah Esfahani fullname: Kouhpah Esfahani, Kiana – sequence: 2 givenname: Behnam surname: Mohammad Hasani Zade fullname: Mohammad Hasani Zade, Behnam – sequence: 3 givenname: Najme surname: Mansouri fullname: Mansouri, Najme email: n.mansouri@uk.ac.ir |
| BookMark | eNqFkMFOwzAMhnMYEtvgFVBeoKNJk269ARMwxNAuII6Rk7pbqrSZknYSPD2tBmdOln_7_2V_MzJpfYuE3LB0wVKW39YLc8DGO9ALnnIxiJIXckKmw7BIliIrLsksxjode8Gm5PWtd51NvK7RdPaEtELo-oA0ohsV31Jwex9sd2hoH227pw_o-j3QzwM4pLtjZxv7DePmFbmowEW8_q1z8vH0-L7eJNvd88v6fpsYLosuEQBCarmqGMsMmnLJIM8Z50JDroU0AIUs0SylXoHIS55mpWS84iCrDKTGbE7yc64JPsaAlToG20D4UixVIwZVqz8MasSgzhgG493ZiMN1J4tBRWOxNVjaMDyrSm__i_gBNVhvPA |
| Cites_doi | 10.1016/j.compbiomed.2022.105356 10.1109/ACCESS.2019.2906757 10.1016/j.asoc.2024.112121 10.1016/j.asoc.2023.110558 10.1016/j.knosys.2023.111084 10.1016/j.knosys.2021.107638 10.1109/TSMCB.2012.2227469 10.1007/s00366-021-01369-9 10.1016/j.eswa.2023.120455 10.1016/j.asoc.2024.112042 10.1007/s11229-021-03233-1 10.1007/s00158-005-0527-z 10.3390/en16010471 10.1016/j.eswa.2023.120652 10.1016/j.cmpb.2008.01.003 10.1007/s10489-017-1019-8 10.1016/j.knosys.2022.109215 10.1109/ACCESS.2020.3000040 10.1109/TKDE.2019.2959988 10.1016/j.eswa.2021.114737 10.1007/s10710-005-6164-x 10.3390/axioms8030079 10.1007/s11227-020-03378-9 10.1016/j.swevo.2012.09.002 10.1109/ACCESS.2020.3047936 10.1080/03052150210915 10.1023/A:1010933404324 10.1109/TCYB.2020.3015756 10.1016/j.swevo.2024.101760 10.1016/j.knosys.2015.12.022 10.1109/TEVC.2023.3292527 10.1016/j.advengsoft.2016.01.008 10.1016/j.eswa.2019.112824 10.1016/j.kjs.2023.02.009 10.1016/j.ins.2022.12.117 10.1155/2024/6769271 10.1007/BF00058655 10.1016/j.imu.2023.101232 10.1007/s10462-020-09952-0 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. |
| Copyright_xml | – notice: 2024 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.chemolab.2024.105295 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Chemistry |
| ExternalDocumentID | 10_1016_j_chemolab_2024_105295 S0169743924002351 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 29B 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AARLI AAXKI AAXUO ABAOU ABFRF ABJNI ABMAC ACDAQ ACGFO ACGFS ACRLP ADBBV ADECG ADEZE ADGUI AEBSH AEFWE AEIPS AEKER AENEX AFJKZ AFKWA AFTJW AFZHZ AGHFR AGUBO AGYEJ AHHHB AIEXJ AIGVJ AIKHN AITUG AJOXV AJSZI AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARUGR AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FLBIZ FNPLU FYGXN G-Q GBLVA IHE J1W KOM MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SCH SDF SDG SDP SES SEW SPC SPCBC SSK SSW SSZ T5K UNMZH YK3 ~02 ~G- 9DU AAQXK AATTM AAYWO AAYXX ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEUPX AFFNX AFPUW AGQPQ AIGII AIIUN AJQLL AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB HMU HVGLF HZ~ M36 M41 R2- SCB WUQ XPP ~HD |
| ID | FETCH-LOGICAL-c259t-4aa45b58f113cecd71a661224ba6b45caa95dec75b8a46d203d512f2a5f3a5be3 |
| ISICitedReferencesCount | 4 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001383855000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0169-7439 |
| IngestDate | Sat Nov 29 05:18:49 EST 2025 Sat Feb 01 16:09:37 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Beluga Whale Optimization Feature selection Multi-objective optimization Random forest |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c259t-4aa45b58f113cecd71a661224ba6b45caa95dec75b8a46d203d512f2a5f3a5be3 |
| ParticipantIDs | crossref_primary_10_1016_j_chemolab_2024_105295 elsevier_sciencedirect_doi_10_1016_j_chemolab_2024_105295 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-15 |
| PublicationDateYYYYMMDD | 2025-02-15 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Chemometrics and intelligent laboratory systems |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Nasibi, Manita, Korbaa (bib7) 2023; 49 Deng, Li, Wang, Cao, Li (bib10) 2023; 110765 Breiman (bib19) 1996; 24 Premkumar, Jangir, Sowmya, Alhelou, Heidari, Chen (bib43) 2021; 9 Zhao, Lv, Xiao, Ma, Pan (bib28) 2024; 165 mirjalili (bib36) 2016; 96 Nakamura, Pereira, Rodrigues, Costa, Papa, Yang (bib37) 2013 Coello Coello, Reyes Sierra (bib45) 2005; 2972 Wang, Zu, Ji, Li, Lu (bib2) 2023; 37 Wang, Xue, Liang, Zhang (bib30) 2023; 626 Breiman (bib20) 2001; 45 [accessed on January, 2023]. Mirjalili, Lewis (bib31) 2013; 9 Olabi, Rezk, Abdelkareem, Awotwe, Maghrabie, Selim, Rahman, Shah, Zaky (bib49) 2023; 16 Al-Tashi, Abdulkadir, Rais, Mirjalili, Alhussian, Ragab, Alqushaibi (bib41) 2020; 8 Too, Abdullah, Saad (bib38) 2019; 8 Sharma, Khodadadi, Saha, Gharehchopogh, Mirjalili (bib42) 2023; 20 Hu, Du, Wang, Wei (bib13) 2022; 235 Abdollahzadeh, Gharehchopogh (bib6) 2022; 38 Hu, Zhang, Gong (bib23) 2021; 51 Coello, Cortes (bib46) 2005; 6 Hoarau, Martin, Dobis, Gall (bib21) 2023; 230 Yan, Wang, Xie (bib14) 2008; 90 Mirjalili, Lewis (bib39) 2016; 95 Mirjalili, Mirjalili, Faris, Aljarah (bib33) 2018; 48 Xue, Zhang, Browne (bib40) 2013; 43 Li, Luo, Zhang, Chen, Zhou (bib26) 2024; 36 Al-Tashi, Abdulkadir, Rais, Mirjalili, Alhussian (bib44) 2019; 7 Ezugwu, Shukla, Nath, Akinyelu, Agushaka, Chiroma, Muhuri (bib4) 2021; 54 Nouri-Moghaddam, Ghazanfari, Fathian (bib47) 2021; 175 Abdel-Basset, El-Shahat, El-henawy, de Albuquerque, Mirjalili (bib12) 2020; 139 Park, Ho (bib9) 2019; 33 Jiao, Nguyen, Xue, Zhang (bib22) 2024; 28 Nassibi, Manita, Korbaa (bib1) 2023; 49 Xue, Zhang (bib24) 2024; 91 Coello Coello, Pulido (bib50) 2005; 30 Mantegna (bib18) 1994; 49 Kanyongo, Ezugvu (bib16) 2023; 38 Sterkenburg, Grünwald (bib5) 2021; 199 Ma, Xu, Ju (bib3) 2023; 229 Hamadni, Won, Alimi, Karray (bib32) 2007 Ismail, Sandell (bib8) 2021; 69 Li, Kang, Li, Pang, Sun, Liang (bib27) 2024; 165 Too, Abdullah (bib35) 2021; 77 Li, Fu, Li, Ding, Lin, Zheng (bib29) 2023; 145 Papasani, Devarakonda (bib15) 2023; 50 Zhong, Li, Meng (bib17) 2022; 251 Hassan, Cotfas, Rezk, Youssef, Shehata, El-Bary (bib48) 2024; 2024 Xue, Zhang, Neri, Gabbouj, Zhang (bib25) 2023; 281 Ray, Liew (bib51) 2002; 34 K. Bache, M. Lichman. UCI Machine Learning Repository [online] Available Liu, Wei, Heidari, Kuang, Zhang, Gui, Chen, Pan (bib11) 2022; 144 Abdel-Basset (10.1016/j.chemolab.2024.105295_bib12) 2020; 139 Xue (10.1016/j.chemolab.2024.105295_bib25) 2023; 281 Hassan (10.1016/j.chemolab.2024.105295_bib48) 2024; 2024 Sterkenburg (10.1016/j.chemolab.2024.105295_bib5) 2021; 199 Li (10.1016/j.chemolab.2024.105295_bib29) 2023; 145 Mirjalili (10.1016/j.chemolab.2024.105295_bib31) 2013; 9 Hu (10.1016/j.chemolab.2024.105295_bib23) 2021; 51 Wang (10.1016/j.chemolab.2024.105295_bib30) 2023; 626 Breiman (10.1016/j.chemolab.2024.105295_bib19) 1996; 24 Olabi (10.1016/j.chemolab.2024.105295_bib49) 2023; 16 Zhao (10.1016/j.chemolab.2024.105295_bib28) 2024; 165 Li (10.1016/j.chemolab.2024.105295_bib26) 2024; 36 Wang (10.1016/j.chemolab.2024.105295_bib2) 2023; 37 Abdollahzadeh (10.1016/j.chemolab.2024.105295_bib6) 2022; 38 Zhong (10.1016/j.chemolab.2024.105295_bib17) 2022; 251 Mirjalili (10.1016/j.chemolab.2024.105295_bib33) 2018; 48 Coello Coello (10.1016/j.chemolab.2024.105295_bib50) 2005; 30 Mantegna (10.1016/j.chemolab.2024.105295_bib18) 1994; 49 Nasibi (10.1016/j.chemolab.2024.105295_bib7) 2023; 49 Al-Tashi (10.1016/j.chemolab.2024.105295_bib44) 2019; 7 Coello (10.1016/j.chemolab.2024.105295_bib46) 2005; 6 Deng (10.1016/j.chemolab.2024.105295_bib10) 2023; 110765 Breiman (10.1016/j.chemolab.2024.105295_bib20) 2001; 45 Hamadni (10.1016/j.chemolab.2024.105295_bib32) 2007 Too (10.1016/j.chemolab.2024.105295_bib35) 2021; 77 Papasani (10.1016/j.chemolab.2024.105295_bib15) 2023; 50 mirjalili (10.1016/j.chemolab.2024.105295_bib36) 2016; 96 Coello Coello (10.1016/j.chemolab.2024.105295_bib45) 2005; 2972 Kanyongo (10.1016/j.chemolab.2024.105295_bib16) 2023; 38 Premkumar (10.1016/j.chemolab.2024.105295_bib43) 2021; 9 Jiao (10.1016/j.chemolab.2024.105295_bib22) 2024; 28 10.1016/j.chemolab.2024.105295_bib34 Ray (10.1016/j.chemolab.2024.105295_bib51) 2002; 34 Al-Tashi (10.1016/j.chemolab.2024.105295_bib41) 2020; 8 Li (10.1016/j.chemolab.2024.105295_bib27) 2024; 165 Sharma (10.1016/j.chemolab.2024.105295_bib42) 2023; 20 Mirjalili (10.1016/j.chemolab.2024.105295_bib39) 2016; 95 Hoarau (10.1016/j.chemolab.2024.105295_bib21) 2023; 230 Ma (10.1016/j.chemolab.2024.105295_bib3) 2023; 229 Park (10.1016/j.chemolab.2024.105295_bib9) 2019; 33 Xue (10.1016/j.chemolab.2024.105295_bib24) 2024; 91 Ismail (10.1016/j.chemolab.2024.105295_bib8) 2021; 69 Nouri-Moghaddam (10.1016/j.chemolab.2024.105295_bib47) 2021; 175 Yan (10.1016/j.chemolab.2024.105295_bib14) 2008; 90 Hu (10.1016/j.chemolab.2024.105295_bib13) 2022; 235 Ezugwu (10.1016/j.chemolab.2024.105295_bib4) 2021; 54 Xue (10.1016/j.chemolab.2024.105295_bib40) 2013; 43 Liu (10.1016/j.chemolab.2024.105295_bib11) 2022; 144 Too (10.1016/j.chemolab.2024.105295_bib38) 2019; 8 Nakamura (10.1016/j.chemolab.2024.105295_bib37) 2013 Nassibi (10.1016/j.chemolab.2024.105295_bib1) 2023; 49 |
| References_xml | – volume: 38 start-page: 1845 year: 2022 end-page: 1863 ident: bib6 article-title: A multi-objective optimization algorithm for feature selection problems publication-title: Eng. Comput. – volume: 77 start-page: 2844 year: 2021 end-page: 2874 ident: bib35 article-title: A new and fast rival genetic algorithm for feature selection publication-title: J. Supercomput. – volume: 9 start-page: 1 year: 2013 end-page: 14 ident: bib31 article-title: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization publication-title: Swarm Evol. Comput. – volume: 48 start-page: 805 year: 2018 end-page: 820 ident: bib33 article-title: Grasshopper optimization algorithm for multi-objective optimization problems publication-title: Appl. Intell. – volume: 251 year: 2022 ident: bib17 article-title: Beluga whale optimization: a novel nature-inspired metaheuristic algorithm publication-title: Knowl. Base Syst. – volume: 91 year: 2024 ident: bib24 article-title: A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems publication-title: Swarm Evol. Comput. – volume: 8 start-page: 106247 year: 2020 end-page: 106263 ident: bib41 article-title: Binary multi-objective Grey Wolf Optimizer for feature selection in classification publication-title: IEEE Access – volume: 165 year: 2024 ident: bib27 article-title: Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning publication-title: Appl. Soft Comput. – volume: 49 start-page: 4677 year: 1994 end-page: 4683 ident: bib18 article-title: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes publication-title: Phys. Rev. – volume: 6 start-page: 163 year: 2005 end-page: 190 ident: bib46 article-title: Solving multiobjective optimization problems using an artificial immune system publication-title: Genet. Program. Evolvable Mach. – volume: 50 start-page: 53 year: 2023 end-page: 64 ident: bib15 article-title: A novel feature selection algorithm using decomposition based multi-objective guided honey badger algorithm (MO-GHBA) and NSGA-III publication-title: Kuwait J. Sci. – volume: 144 year: 2022 ident: bib11 article-title: Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis publication-title: Comput. Biol. Med. – volume: 2024 year: 2024 ident: bib48 article-title: Ideal parameter estimation of photocatalysis process to boost amoxicillin degradation efficiency using marine predators optimization algorithm publication-title: Int. J. Photoenergy – volume: 110765 year: 2023 ident: bib10 article-title: A feature-thresholds guided genetic algorithm based on a multi-objective feature scoring method for high-dimensional feature selection publication-title: Appl. Soft Comput. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib20 article-title: Random forests publication-title: Mach. Learn. – volume: 69 start-page: 424 year: 2021 end-page: 428 ident: bib8 article-title: A low-complexity endurance modulation for flash memory publication-title: IEEE Trans. Circ. Syst. II: Express Briefs – volume: 8 start-page: 79 year: 2019 ident: bib38 article-title: Hybrid binary particle swarm optimization differential evolution-based feature selection for EMG signals classification publication-title: Axioms – reference: . [accessed on January, 2023]. – volume: 30 start-page: 388 year: 2005 end-page: 403 ident: bib50 article-title: Multiobjective structural optimization using a microgenetic algorithm publication-title: Struct. Multidiscip. Optim. – volume: 7 start-page: 39496 year: 2019 end-page: 39508 ident: bib44 article-title: Binary optimization using hybrid Grey Wolf Optimization for feature selection publication-title: IEEE Access – volume: 145 year: 2023 ident: bib29 article-title: Multi-objective binary grey wolf optimization for feature selection based on guided mutation strategy publication-title: Appl. Soft Comput. – volume: 54 start-page: 4237 year: 2021 end-page: 4316 ident: bib4 article-title: Metaheuristics: a comprehensive overview and classification along with bibliometric analysis publication-title: Artif. Intell. Rev. – volume: 229 year: 2023 ident: bib3 article-title: Class-specific feature selection via maximal dynamic correlation change and minimal redundancy publication-title: Expert Syst. Appl. – volume: 165 year: 2024 ident: bib28 article-title: Hierarchical learning multi-objective firefly algorithm for high-dimensional feature selection publication-title: Appl. Soft Comput. – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: bib36 article-title: SCA: a Sine Cosine Algorithm for solving optimization problems publication-title: Knowl. Base Syst. – volume: 199 start-page: 9979 year: 2021 end-page: 10015 ident: bib5 article-title: The no-free-lunch theorems of supervised learning publication-title: Synthese – volume: 2972 start-page: 688 year: 2005 end-page: 697 ident: bib45 article-title: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm publication-title: Adv. Artif. Intell. Berlin – volume: 43 start-page: 1656 year: 2013 end-page: 1671 ident: bib40 article-title: Particle Swarm Optimization for feature selection in classification: a multi-objective approach publication-title: IEEE Trans. Cybern. – volume: 175 year: 2021 ident: bib47 article-title: A novel multi-objective forest optimization algorithm for wrapper feature selection publication-title: Expert Syst. Appl. – volume: 281 year: 2023 ident: bib25 article-title: An external attention-based feature ranker for large-scale feature selection publication-title: Knowl. Base Syst. – volume: 49 year: 2023 ident: bib7 article-title: Advances in nature-inspired metaheuristic optimization for feature selection problem: a comprehensive survey publication-title: Comput. Sci. Rev. – volume: 28 start-page: 1156 year: 2024 end-page: 1176 ident: bib22 article-title: A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges publication-title: IEEE Trans. Evol. Comput. – volume: 235 year: 2022 ident: bib13 article-title: An enhanced black widow optimization algorithm for feature selection publication-title: Knowl. Base Syst. – volume: 38 year: 2023 ident: bib16 article-title: Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives publication-title: Inform. Med. Unlocked – start-page: 240 year: 2007 end-page: 247 ident: bib32 article-title: Multi-objective Feature Selection with NSGA II, Adaptive and Natural Computing Algorithms Berlin – volume: 49 year: 2023 ident: bib1 article-title: Advances in nature-inspired metaheuristic optimization for feature selection problem: a comprehensive survey publication-title: Comput. Sci. Rev. – year: 2013 ident: bib37 article-title: Binary Bat algorithm for feature selection publication-title: Swarm Intelligence and Bio-Inspired Computation – reference: K. Bache, M. Lichman. UCI Machine Learning Repository [online] Available: – volume: 20 start-page: 819 year: 2023 end-page: 843 ident: bib42 article-title: Non-dominated sorting advanced butterfly optimization algorithm for multi-objective problems publication-title: JBE – volume: 9 start-page: 3229 year: 2021 end-page: 3248 ident: bib43 article-title: MOSMA: multi-objective slime mould algorithm based on elitist non-dominated sorting publication-title: IEEE Access – volume: 16 start-page: 471 year: 2023 ident: bib49 article-title: Optimal parameter identification of perovskite solar cells using modified bald eagle search optimization algorithm publication-title: Energies – volume: 37 year: 2023 ident: bib2 article-title: MIC-SHAP: an ensemble feature selection method for materials machine learning publication-title: Mater. Today Commun. – volume: 51 start-page: 874 year: 2021 end-page: 888 ident: bib23 article-title: Multiobjective particle swarm optimization for feature selection with fuzzy cost publication-title: IEEE Trans. Cybern. – volume: 33 start-page: 2995 year: 2019 end-page: 3006 ident: bib9 article-title: Tackling overfitting in boosting for noisy healthcare data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 90 start-page: 275 year: 2008 end-page: 284 ident: bib14 article-title: The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification publication-title: Comput. Methods Progr. Biomed. – volume: 230 year: 2023 ident: bib21 article-title: Evidential random forests publication-title: Expert Syst. Appl. – volume: 36 year: 2024 ident: bib26 article-title: IMOABC: an efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection publication-title: J. King Saud Univ. Comput. Inf. Sci. – volume: 34 start-page: 141 year: 2002 end-page: 153 ident: bib51 article-title: A swarm metaphor for multiobjective design optimization publication-title: Eng. Optim. – volume: 139 year: 2020 ident: bib12 article-title: A new fusion of grey wolf optimizer algorithm with a twophase mutation for feature selection publication-title: Expert Syst. Appl. – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: bib39 article-title: The whale optimization algorithm publication-title: Adv. Eng. Software – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: bib19 article-title: Bagging predictors publication-title: Mach. Learn. – volume: 626 start-page: 586 year: 2023 end-page: 606 ident: bib30 article-title: Feature selection using diversity-based multi-objective binary differential evolution publication-title: Inf. Sci. – volume: 144 year: 2022 ident: 10.1016/j.chemolab.2024.105295_bib11 article-title: Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105356 – volume: 7 start-page: 39496 year: 2019 ident: 10.1016/j.chemolab.2024.105295_bib44 article-title: Binary optimization using hybrid Grey Wolf Optimization for feature selection publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2906757 – volume: 165 year: 2024 ident: 10.1016/j.chemolab.2024.105295_bib27 article-title: Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2024.112121 – volume: 145 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib29 article-title: Multi-objective binary grey wolf optimization for feature selection based on guided mutation strategy publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2023.110558 – volume: 281 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib25 article-title: An external attention-based feature ranker for large-scale feature selection publication-title: Knowl. Base Syst. doi: 10.1016/j.knosys.2023.111084 – year: 2013 ident: 10.1016/j.chemolab.2024.105295_bib37 article-title: Binary Bat algorithm for feature selection – volume: 235 year: 2022 ident: 10.1016/j.chemolab.2024.105295_bib13 article-title: An enhanced black widow optimization algorithm for feature selection publication-title: Knowl. Base Syst. doi: 10.1016/j.knosys.2021.107638 – volume: 43 start-page: 1656 year: 2013 ident: 10.1016/j.chemolab.2024.105295_bib40 article-title: Particle Swarm Optimization for feature selection in classification: a multi-objective approach publication-title: IEEE Trans. Cybern. doi: 10.1109/TSMCB.2012.2227469 – volume: 38 start-page: 1845 year: 2022 ident: 10.1016/j.chemolab.2024.105295_bib6 article-title: A multi-objective optimization algorithm for feature selection problems publication-title: Eng. Comput. doi: 10.1007/s00366-021-01369-9 – volume: 229 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib3 article-title: Class-specific feature selection via maximal dynamic correlation change and minimal redundancy publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.120455 – volume: 36 year: 2024 ident: 10.1016/j.chemolab.2024.105295_bib26 article-title: IMOABC: an efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection publication-title: J. King Saud Univ. Comput. Inf. Sci. – volume: 165 year: 2024 ident: 10.1016/j.chemolab.2024.105295_bib28 article-title: Hierarchical learning multi-objective firefly algorithm for high-dimensional feature selection publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2024.112042 – volume: 199 start-page: 9979 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib5 article-title: The no-free-lunch theorems of supervised learning publication-title: Synthese doi: 10.1007/s11229-021-03233-1 – volume: 30 start-page: 388 year: 2005 ident: 10.1016/j.chemolab.2024.105295_bib50 article-title: Multiobjective structural optimization using a microgenetic algorithm publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-005-0527-z – volume: 16 start-page: 471 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib49 article-title: Optimal parameter identification of perovskite solar cells using modified bald eagle search optimization algorithm publication-title: Energies doi: 10.3390/en16010471 – volume: 230 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib21 article-title: Evidential random forests publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.120652 – volume: 90 start-page: 275 year: 2008 ident: 10.1016/j.chemolab.2024.105295_bib14 article-title: The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2008.01.003 – volume: 48 start-page: 805 year: 2018 ident: 10.1016/j.chemolab.2024.105295_bib33 article-title: Grasshopper optimization algorithm for multi-objective optimization problems publication-title: Appl. Intell. doi: 10.1007/s10489-017-1019-8 – volume: 251 year: 2022 ident: 10.1016/j.chemolab.2024.105295_bib17 article-title: Beluga whale optimization: a novel nature-inspired metaheuristic algorithm publication-title: Knowl. Base Syst. doi: 10.1016/j.knosys.2022.109215 – volume: 8 start-page: 106247 year: 2020 ident: 10.1016/j.chemolab.2024.105295_bib41 article-title: Binary multi-objective Grey Wolf Optimizer for feature selection in classification publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3000040 – volume: 49 start-page: 4677 year: 1994 ident: 10.1016/j.chemolab.2024.105295_bib18 article-title: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes publication-title: Phys. Rev. – ident: 10.1016/j.chemolab.2024.105295_bib34 – volume: 33 start-page: 2995 year: 2019 ident: 10.1016/j.chemolab.2024.105295_bib9 article-title: Tackling overfitting in boosting for noisy healthcare data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2959988 – volume: 175 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib47 article-title: A novel multi-objective forest optimization algorithm for wrapper feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114737 – volume: 20 start-page: 819 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib42 article-title: Non-dominated sorting advanced butterfly optimization algorithm for multi-objective problems publication-title: JBE – volume: 49 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib1 article-title: Advances in nature-inspired metaheuristic optimization for feature selection problem: a comprehensive survey publication-title: Comput. Sci. Rev. – volume: 6 start-page: 163 year: 2005 ident: 10.1016/j.chemolab.2024.105295_bib46 article-title: Solving multiobjective optimization problems using an artificial immune system publication-title: Genet. Program. Evolvable Mach. doi: 10.1007/s10710-005-6164-x – volume: 37 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib2 article-title: MIC-SHAP: an ensemble feature selection method for materials machine learning publication-title: Mater. Today Commun. – start-page: 240 year: 2007 ident: 10.1016/j.chemolab.2024.105295_bib32 – volume: 8 start-page: 79 year: 2019 ident: 10.1016/j.chemolab.2024.105295_bib38 article-title: Hybrid binary particle swarm optimization differential evolution-based feature selection for EMG signals classification publication-title: Axioms doi: 10.3390/axioms8030079 – volume: 77 start-page: 2844 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib35 article-title: A new and fast rival genetic algorithm for feature selection publication-title: J. Supercomput. doi: 10.1007/s11227-020-03378-9 – volume: 9 start-page: 1 year: 2013 ident: 10.1016/j.chemolab.2024.105295_bib31 article-title: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2012.09.002 – volume: 9 start-page: 3229 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib43 article-title: MOSMA: multi-objective slime mould algorithm based on elitist non-dominated sorting publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3047936 – volume: 49 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib7 article-title: Advances in nature-inspired metaheuristic optimization for feature selection problem: a comprehensive survey publication-title: Comput. Sci. Rev. – volume: 34 start-page: 141 year: 2002 ident: 10.1016/j.chemolab.2024.105295_bib51 article-title: A swarm metaphor for multiobjective design optimization publication-title: Eng. Optim. doi: 10.1080/03052150210915 – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.chemolab.2024.105295_bib20 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 69 start-page: 424 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib8 article-title: A low-complexity endurance modulation for flash memory publication-title: IEEE Trans. Circ. Syst. II: Express Briefs – volume: 51 start-page: 874 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib23 article-title: Multiobjective particle swarm optimization for feature selection with fuzzy cost publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.3015756 – volume: 91 year: 2024 ident: 10.1016/j.chemolab.2024.105295_bib24 article-title: A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2024.101760 – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.chemolab.2024.105295_bib36 article-title: SCA: a Sine Cosine Algorithm for solving optimization problems publication-title: Knowl. Base Syst. doi: 10.1016/j.knosys.2015.12.022 – volume: 28 start-page: 1156 year: 2024 ident: 10.1016/j.chemolab.2024.105295_bib22 article-title: A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2023.3292527 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.chemolab.2024.105295_bib39 article-title: The whale optimization algorithm publication-title: Adv. Eng. Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 139 year: 2020 ident: 10.1016/j.chemolab.2024.105295_bib12 article-title: A new fusion of grey wolf optimizer algorithm with a twophase mutation for feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.112824 – volume: 50 start-page: 53 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib15 article-title: A novel feature selection algorithm using decomposition based multi-objective guided honey badger algorithm (MO-GHBA) and NSGA-III publication-title: Kuwait J. Sci. doi: 10.1016/j.kjs.2023.02.009 – volume: 626 start-page: 586 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib30 article-title: Feature selection using diversity-based multi-objective binary differential evolution publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.12.117 – volume: 2024 year: 2024 ident: 10.1016/j.chemolab.2024.105295_bib48 article-title: Ideal parameter estimation of photocatalysis process to boost amoxicillin degradation efficiency using marine predators optimization algorithm publication-title: Int. J. Photoenergy doi: 10.1155/2024/6769271 – volume: 110765 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib10 article-title: A feature-thresholds guided genetic algorithm based on a multi-objective feature scoring method for high-dimensional feature selection publication-title: Appl. Soft Comput. – volume: 24 start-page: 123 year: 1996 ident: 10.1016/j.chemolab.2024.105295_bib19 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1007/BF00058655 – volume: 38 year: 2023 ident: 10.1016/j.chemolab.2024.105295_bib16 article-title: Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives publication-title: Inform. Med. Unlocked doi: 10.1016/j.imu.2023.101232 – volume: 2972 start-page: 688 year: 2005 ident: 10.1016/j.chemolab.2024.105295_bib45 article-title: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm publication-title: Adv. Artif. Intell. Berlin – volume: 54 start-page: 4237 year: 2021 ident: 10.1016/j.chemolab.2024.105295_bib4 article-title: Metaheuristics: a comprehensive overview and classification along with bibliometric analysis publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09952-0 |
| SSID | ssj0016941 |
| Score | 2.461189 |
| Snippet | The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection,... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 105295 |
| SubjectTerms | Beluga Whale Optimization Feature selection Multi-objective optimization Random forest |
| Title | Multi-objective feature selection algorithm using Beluga Whale Optimization |
| URI | https://dx.doi.org/10.1016/j.chemolab.2024.105295 |
| Volume | 257 |
| WOSCitedRecordID | wos001383855000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0169-7439 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0016941 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9swEBcjHWwvY5-s3Qd-2JvwlshWbD22JaNbWTdYx8JezMmW6oTaDrFT-uf39BHXtIVtjL2Y4GAlvvuhO530-x0h7wx9Ewpz5klLGcZJMQ5FwUQohFRJzkFPU7DNJpKTk3Q-F9_8kaDWthNI6jq9vBSr_-pqvIfONtTZv3B3PyjewM_odLyi2_H6R463lNqwkUs3lVGtrHYnbW3HG3v6-PysWS-6sqIbWyk4UOebM6A_SwwW9CvOIZUnZw4zV6Ms0FSm_5aXdV70Yp4d9VAy-_XtQALdUmo25QpKOms1lK5_FD1GSPbR4EtTQlVBQY-gxe_pLy8EfKDKGqrrgnltthkWLiAsvcKCr1Ywy_52fE1XQrtFo3FVzakIzcpoOC0zJ1x9a4p31Ybl-9y8Nr4ervFZbNoVM9et84Z89nczuBnbHJZlkWHb77CEi3REdvY_zeaf-z0nQ-l1SvDuzwz45Hf_2t2pzCA9OX1MHvl1RbDv8PCE3FP1U_LgcNvO7xk5voGLwOMi6HER9LgILC4Ch4vA4iIY4uI5-fFxdnp4FPpOGmGOy9sujAFiLnmqJ5MoV3mRTADzMszeJExlzHMAwQuVJ1ymEE8LNo4KTAQ1A64j4FJFL8iobmr1kgS6YDkoBXGk0ngcRVJLjUEBF848Aoyvu-TD1ijZygmmZNuThMtsa8bMmDFzZtwlYmu7zKd9Lp3L0OW_eXbvH559RR5eI_Q1GXXrjXpD7ucX3aJdv_XouALY5oWn |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multi-objective+feature+selection+algorithm+using+Beluga+Whale+Optimization&rft.jtitle=Chemometrics+and+intelligent+laboratory+systems&rft.au=Kouhpah+Esfahani%2C+Kiana&rft.au=Mohammad+Hasani+Zade%2C+Behnam&rft.au=Mansouri%2C+Najme&rft.date=2025-02-15&rft.pub=Elsevier+B.V&rft.issn=0169-7439&rft.volume=257&rft_id=info:doi/10.1016%2Fj.chemolab.2024.105295&rft.externalDocID=S0169743924002351 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0169-7439&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0169-7439&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0169-7439&client=summon |