A Hybrid Feature Selection Method using an Improved Binary Butterfly Optimization Algorithm and Adaptive β -Hill Climbing
The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent...
Uložené v:
| Vydané v: | IEEE access Ročník 11; s. 1 |
|---|---|
| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Piscataway
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 2169-3536, 2169-3536 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive β -Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: N -operator (Neighborhood operator) and β -operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach's time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method balances space exploration and solution exploitation in feature selection problems. |
|---|---|
| AbstractList | The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is shown to be superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and hence, produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive <tex-math notation="LaTeX">$\beta -$ </tex-math>Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: <tex-math notation="LaTeX">$N$ </tex-math>-operator (Neighborhood operator) and <tex-math notation="LaTeX">$\beta $ </tex-math>-operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach's time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method effectively balances space exploration and solution exploitation in feature selection problems. The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is shown to be superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and hence, produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive [Formula Omitted]Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: [Formula Omitted]-operator (Neighborhood operator) and [Formula Omitted]-operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach’s time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method effectively balances space exploration and solution exploitation in feature selection problems. The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive β -Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: N -operator (Neighborhood operator) and β -operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach's time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method balances space exploration and solution exploitation in feature selection problems. |
| Author | Tiwari, Anurag |
| Author_xml | – sequence: 1 givenname: Anurag surname: Tiwari fullname: Tiwari, Anurag organization: Thapar Institute of Engineering and Technology, Patiala, Punjab, India |
| BookMark | eNp9kc9u1DAQhyNUJErpE8DBEucs_pM49jGNWnaloh4WzpZjT7ZeJfHiOJW2j8WD8Ex4N0WqOOCLrfF8n0bze59djH6ELPtI8IoQLL_UTXO73a4opmzFaFUUXL7JLinhMmcl4xev3u-y62na43REKpXVZfZco_WxDc6iO9BxDoC20IOJzo_oG8RHb9E8uXGH9Ig2wyH4J7Doxo06HNHNHCOErj-ih0N0g3vWZ6zudz64-DgkxqLa6vT5BOj3L5SvXd-jpndDm5Qfsred7ie4frmvsh93t9-bdX7_8HXT1Pe5YbSQedvx1nAGGjjjhcWcSEJEV2nMDOO8EkCsLlqNO8KkFbITRlSmq8rW4EJqy66yzeK1Xu_VIbghDa-8dupc8GGndIjO9KAAtzSJAZIhsVhg3hnLBCUguWAn1-fFlTbxc4Ypqr2fw5jGV1RwUpRliiR1yaXLBD9NATplXDwvJwbtekWwOiWnluTUKTn1klxi2T_s34n_T31aKAcArwhCSUEl-wPJ-6e7 |
| CODEN | IAECCG |
| CitedBy_id | crossref_primary_10_1016_j_rineng_2025_104262 crossref_primary_10_1109_ACCESS_2024_3514781 crossref_primary_10_1109_TAI_2023_3334707 crossref_primary_10_1109_ACCESS_2024_3508028 crossref_primary_10_15832_ankutbd_1537267 crossref_primary_10_1016_j_dt_2025_06_019 crossref_primary_10_1109_ACCESS_2023_3342064 crossref_primary_10_1007_s42044_025_00252_w |
| Cites_doi | 10.1016/j.engappai.2020.104079 10.1109/ICSPIS51611.2020.9349589 10.1504/IJMHEUR.2018.091880 10.1007/s00521-013-1525-5 10.1109/ICNN.1995.488968 10.1016/j.knosys.2020.106560 10.1007/s00500-018-3102-4 10.1504/IJBIC.2010.032124 10.1109/ACCESS.2021.3110882 10.1016/j.neucom.2015.06.083 10.1109/ACCESS.2020.2985986 10.1016/j.eswa.2019.06.044 10.1016/j.eswa.2018.08.051 10.1016/j.asoc.2007.05.007 10.1007/s00521-015-1923-y 10.1007/s12652-019-01324-z 10.1109/ACCESS.2020.3033757 10.1016/j.amc.2020.125535 10.1007/s00521-013-1367-1 10.1016/j.plrev.2005.10.001 10.1016/j.knosys.2015.12.022 10.1007/s11047-009-9175-3 10.1109/TEVC.2010.2059031 10.1016/j.jksuci.2019.11.007 10.1016/j.compbiomed.2021.105152 10.1016/j.eswa.2022.116621 10.1007/s11222-009-9153-8 10.3233/JIFS-16798 10.1038/scientificamerican0792-66 10.1007/s12652-020-02484-z 10.1109/ICRITO.2018.8748282 10.1016/j.advengsoft.2015.01.010 10.1007/s00500-020-04812-z 10.1007/s10489-017-1019-8 10.1016/j.knosys.2019.105190 10.1145/1143997.1144177 10.1016/j.eswa.2022.117757 10.1007/BF00994018 10.1016/j.eswa.2023.119921 10.1007/s00500-019-03887-7 10.1080/019697298125470 10.1016/j.ssci.2011.08.065 10.1007/s10489-017-0903-6 10.1016/j.advengsoft.2013.12.007 10.1109/ACCESS.2018.2818682 10.1109/MED.2007.4433821 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2023.3274469 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 1 |
| ExternalDocumentID | oai_doaj_org_article_e0b27a0ee87c49a0806fcd3821e9683d 10_1109_ACCESS_2023_3274469 10121429 |
| Genre | orig-research |
| GroupedDBID | 0R~ 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS 4.4 AAYXX AGSQL CITATION EJD 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c3249-bf6bc63eae6364d0619118f7a03c36678e1da4ba0f139d89f8c87cf75bc049ad3 |
| IEDL.DBID | DOA |
| ISSN | 2169-3536 |
| IngestDate | Fri Oct 03 12:52:42 EDT 2025 Mon Jun 30 05:51:52 EDT 2025 Sat Nov 29 04:02:37 EST 2025 Tue Nov 18 21:22:39 EST 2025 Wed Aug 27 02:18:22 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc-nd/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3249-bf6bc63eae6364d0619118f7a03c36678e1da4ba0f139d89f8c87cf75bc049ad3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-6816-0113 |
| OpenAccessLink | https://doaj.org/article/e0b27a0ee87c49a0806fcd3821e9683d |
| PQID | 2861455110 |
| PQPubID | 4845423 |
| PageCount | 1 |
| ParticipantIDs | crossref_citationtrail_10_1109_ACCESS_2023_3274469 ieee_primary_10121429 doaj_primary_oai_doaj_org_article_e0b27a0ee87c49a0806fcd3821e9683d proquest_journals_2861455110 crossref_primary_10_1109_ACCESS_2023_3274469 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-00-00 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – year: 2023 text: 2023-00-00 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 ref52 ref11 ref10 ref17 ref16 ref19 ref18 woolson (ref39) 2007 ref51 ref50 ref46 ref45 ref48 ref41 ref44 ref43 vapnik (ref28) 1991; 4 ref49 ref8 ref7 murphy (ref31) 1992 ref9 ref4 ref3 ref6 ref5 swinburne (ref29) 2004; 194 ref40 ref35 ref34 ref37 ref36 deep (ref47) 2011; 2 ref30 ref33 ref32 cortes (ref27) 1995; 20 ref2 ref1 ref38 tiwari (ref42) 2023 ref24 ref23 ref26 ref25 ref20 ref22 ref21 |
| References_xml | – ident: ref38 doi: 10.1016/j.engappai.2020.104079 – ident: ref51 doi: 10.1109/ICSPIS51611.2020.9349589 – ident: ref12 doi: 10.1504/IJMHEUR.2018.091880 – ident: ref14 doi: 10.1007/s00521-013-1525-5 – start-page: 1 year: 2007 ident: ref39 article-title: Wilcoxon signed-rank test publication-title: Wiley Encyclopedia of Clinical Trials – ident: ref4 doi: 10.1109/ICNN.1995.488968 – ident: ref10 doi: 10.1016/j.knosys.2020.106560 – ident: ref18 doi: 10.1007/s00500-018-3102-4 – ident: ref22 doi: 10.1504/IJBIC.2010.032124 – ident: ref41 doi: 10.1109/ACCESS.2021.3110882 – ident: ref15 doi: 10.1016/j.neucom.2015.06.083 – ident: ref37 doi: 10.1109/ACCESS.2020.2985986 – ident: ref8 doi: 10.1016/j.eswa.2019.06.044 – ident: ref50 doi: 10.1016/j.eswa.2018.08.051 – ident: ref19 doi: 10.1016/j.asoc.2007.05.007 – ident: ref6 doi: 10.1007/s00521-015-1923-y – ident: ref11 doi: 10.1007/s12652-019-01324-z – ident: ref36 doi: 10.1109/ACCESS.2020.3033757 – volume: 194 start-page: 250 year: 2004 ident: ref29 article-title: Bayes' theorem publication-title: Revue Philosophique de la France Et de l'Etranger – ident: ref44 doi: 10.1016/j.amc.2020.125535 – ident: ref20 doi: 10.1007/s00521-013-1367-1 – ident: ref5 doi: 10.1016/j.plrev.2005.10.001 – ident: ref34 doi: 10.1016/j.knosys.2015.12.022 – ident: ref17 doi: 10.1007/s11047-009-9175-3 – ident: ref21 doi: 10.1109/TEVC.2010.2059031 – ident: ref13 doi: 10.1016/j.jksuci.2019.11.007 – ident: ref24 doi: 10.1016/j.compbiomed.2021.105152 – ident: ref1 doi: 10.1016/j.eswa.2022.116621 – ident: ref25 doi: 10.1007/s11222-009-9153-8 – ident: ref35 doi: 10.3233/JIFS-16798 – ident: ref3 doi: 10.1038/scientificamerican0792-66 – year: 1992 ident: ref31 publication-title: UCI repository of machine learning databases – ident: ref9 doi: 10.1007/s12652-020-02484-z – ident: ref40 doi: 10.1109/ICRITO.2018.8748282 – ident: ref32 doi: 10.1016/j.advengsoft.2015.01.010 – ident: ref45 doi: 10.1007/s00500-020-04812-z – ident: ref33 doi: 10.1007/s10489-017-1019-8 – ident: ref26 doi: 10.1016/j.knosys.2019.105190 – ident: ref48 doi: 10.1145/1143997.1144177 – volume: 2 start-page: 2 year: 2011 ident: ref47 article-title: New variations of order crossover for travelling salesman problem publication-title: Int J Comb Optim Probl Inform ? 9 – ident: ref49 doi: 10.1016/j.eswa.2022.117757 – volume: 20 start-page: 273 year: 1995 ident: ref27 article-title: Support-vector networks publication-title: Mach Learn doi: 10.1007/BF00994018 – ident: ref52 doi: 10.1016/j.eswa.2023.119921 – ident: ref23 doi: 10.1007/s00500-019-03887-7 – ident: ref43 doi: 10.1080/019697298125470 – ident: ref30 doi: 10.1016/j.ssci.2011.08.065 – ident: ref46 doi: 10.1007/s10489-017-0903-6 – ident: ref7 doi: 10.1016/j.advengsoft.2013.12.007 – ident: ref2 doi: 10.1109/ACCESS.2018.2818682 – volume: 4 start-page: 1 year: 1991 ident: ref28 article-title: Principles of risk minimization for learning theory publication-title: Proc Adv Neural Inf Process Syst – ident: ref16 doi: 10.1109/MED.2007.4433821 – start-page: 1 year: 2023 ident: ref42 article-title: Wilson's disease classification using higher-order Gabor tensors and various classifiers on a small and imbalanced brain MRI dataset publication-title: Multimedia Tools Appl J |
| SSID | ssj0000816957 |
| Score | 2.3544922 |
| Snippet | The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies.... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Adaptive algorithms Butterfly Optimization Algorithm Classification Classification Accuracy Classification algorithms Convergence Convergence Rate Datasets Exploitation Feature extraction Feature Selection Heuristic methods Local Optima Metaheuristics Operators (mathematics) Optimization Optimization algorithms Optimization techniques Search problems Searching Sociology Space exploration State of the art Tradeoffs Transfer Function Transfer functions |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NaxsxEBVN6KE9NP1IiZu06NBj19ldrSXtcWMSfGlaaAu5Ca00cg1rOzh2IflZ-SH9TZnRKsZQWuhtWVashifNjD7mPcY-VtYG1bYuC3S0W6lcZzb3IROe0glhq1r5KDahLi_11VX9NRWrx1oYAIiXz2BIj_Es3y_dhrbKTomLqkAHusf2lJJ9sdZ2Q4UUJOqRSsxCRV6fNuMxGjEkgfChICY8utW8E30iSX9SVfnDFcf4cnHwnz17yV6kRJI3PfKv2BNYvGbPd-gF37C7hk9uqSKLU6K3WQH_FlVvEAr-OSpHc7r2PuV2wfvNBfD8LBbo8iRg3d3yL-hU5qlakzfddLmarX_OsY3njbfX5C7573ueTWZdx8fdbI5r7ekh-3Fx_n08yZLWQuYwpaqzNsjWSQEWpJCVxyiPXlAHZXPhhMSIBoW3VWvzgCmj13XQThOj0ah1uMawXrxl-4vlAo4Yz61FT9AGUSlfySDa4IgYTpQARRgVasDKRwyMS0TkpIfRmbggyWvTA2cIOJOAG7BP20bXPQ_Hvz8_I3C3nxKJdnyBqJk0Jw3kbYkGAqAlaAPmzjI4L3RZQC218AN2SEjv_K8HecBOHseKSTP-xpRaEuc7dujdX5ods2fUxX7_5oTtr1cbeM-eul_r2c3qQxzMD_Pe8xE priority: 102 providerName: IEEE |
| Title | A Hybrid Feature Selection Method using an Improved Binary Butterfly Optimization Algorithm and Adaptive β -Hill Climbing |
| URI | https://ieeexplore.ieee.org/document/10121429 https://www.proquest.com/docview/2861455110 https://doaj.org/article/e0b27a0ee87c49a0806fcd3821e9683d |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3LbtQwFLVQxQIWiEcRU0rlBUvSOnHGj2U6ajWbFiRA6s7ys4yUmVbTKdJs-Kh-SL-Jex23ioQEGzZZRHb8uNf34djnEPKxtTZJ53yV8NduK5mqLAup4gHDCW5bLUMmm5Dn5-riQn8ZUX3hmbABHniYuKPIXCMti1FJ32oLAY5IPnDV1FELxQNaXyb1KJnKNljVQk9lgRmqmT7qZjMY0SGyhR9yhMXDI84jV5QR-wvFyh92OTub05fkRYkSaTf07hV5ElevyfMRduAbsu3ofIvXrShGcbfrSL9mShuYZ3qWaaFpPg5A7YoOOwcx0ON8-5YWdup-Sz-DxViWq5i06y-v1ovNjyXUCbQL9hptIb2_q-aLvqezfrGEPPpyl3w_Pfk2m1eFR6HyEC7pyiXhvODRRsFFG8CDg4VTCWaVey7AW8U62NZZliAcDEon5RWiFU2dh_zBBv6W7KyuVvEdocxaWOUu8VaGViTukkfQN97EWKdpLSekeZhS4wvIOHJd9CYnG0ybQQ4G5WCKHCbk02Ol6wFj4-_Fj1FWj0URIDu_ALUxRW3Mv9RmQnZR0qP2agSfg4_vP4jelNV8YxolEM8dOrT3P9p-T57heIaNnH2ys1nfxg_kqf-5WdysD7Iiw_Ps18lBvo74G1I996E |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3bbhMxELWgIAEPXIsItOAHHtnUu3a8u4_bqFUQbUCiSH2zfE0jbZIqTZDKZ_EhfBMzXjeKhIrUt9VqrfXo2DPjy5xDyEehdSiNsVnAo11RsirTzIWMO0wnuBZ16aLYRDkeV-fn9bdUrB5rYbz38fKZ7-NjPMt3C7vGrbID5KLKwYHeJw8GQhSsK9fabKmghkQ9KBO3UM7qg2Y4BDP6KBHe58iFh_eat-JPpOlPuir_OOMYYY6f3bFvz8nTlErSpsP-Bbnn5y_Jky2CwVfkV0NH11iTRTHVWy89_R51bwAMehq1oylefJ9QPafd9oJ39DCW6NIkYd1e06_gVmapXpM27WSxnK4uZtDG0cbpS3SY9M9vmo2mbUuH7XQGq-3JLvlxfHQ2HGVJbSGzkFTVmQnSWMm99pJL4SDOgx-sQqkZt1xCTPO508JoFiBpdFUdKlshp9HAWFhlaMdfk535Yu7fEMq0Bl9gAhelEzJwEyxSw_HC-zwM8rJHihsMlE1U5KiI0aq4JGG16oBTCJxKwPXIp02jy46J4_-fHyK4m0-RRju-ANRUmpXKM1OAgd6DJWADZM8yWMerIve1rLjrkV1Eeut_Hcg9snczVlSa81eqqCSyvkOH3t7S7AN5NDo7PVEnn8df3pHH2N1uN2eP7KyWa79PHtqfq-nV8n0c2H8BuOz2WA |
| 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=A+Hybrid+Feature+Selection+Method+Using+an+Improved+Binary+Butterfly+Optimization+Algorithm+and+Adaptive+%CE%B2%E2%80%93Hill+Climbing&rft.jtitle=IEEE+access&rft.au=Tiwari%2C+Anurag&rft.date=2023&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.eissn=2169-3536&rft.volume=11&rft.spage=93511&rft_id=info:doi/10.1109%2FACCESS.2023.3274469&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |