Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data
Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accuracy and simultaneously minimize the number of sele...
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| Vydané v: | Mathematics (Basel) Ročník 10; číslo 15; s. 2770 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Basel
MDPI AG
01.08.2022
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| ISSN: | 2227-7390, 2227-7390 |
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| Abstract | Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accuracy and simultaneously minimize the number of selected features. Therefore, this paper is devoted to developing an efficient binary version of the quantum-based avian navigation optimizer algorithm (QANA) named BQANA, utilizing the scalability of the QANA to effectively select the optimal feature subset from high-dimensional medical datasets using two different approaches. In the first approach, several binary versions of the QANA are developed using S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to map the continuous solutions of the canonical QANA to binary ones. In the second approach, the QANA is mapped to binary space by converting each variable to 0 or 1 using a threshold. To evaluate the proposed algorithm, first, all binary versions of the QANA are assessed on different medical datasets with varied feature sizes, including Pima, HeartEW, Lymphography, SPECT Heart, PenglungEW, Parkinson, Colon, SRBCT, Leukemia, and Prostate tumor. The results show that the BQANA developed by the second approach is superior to other binary versions of the QANA to find the optimal feature subset from the medical datasets. Then, the BQANA was compared with nine well-known binary metaheuristic algorithms, and the results were statistically assessed using the Friedman test. The experimental and statistical results demonstrate that the proposed BQANA has merit for feature selection from medical datasets. |
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| AbstractList | Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accuracy and simultaneously minimize the number of selected features. Therefore, this paper is devoted to developing an efficient binary version of the quantum-based avian navigation optimizer algorithm (QANA) named BQANA, utilizing the scalability of the QANA to effectively select the optimal feature subset from high-dimensional medical datasets using two different approaches. In the first approach, several binary versions of the QANA are developed using S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to map the continuous solutions of the canonical QANA to binary ones. In the second approach, the QANA is mapped to binary space by converting each variable to 0 or 1 using a threshold. To evaluate the proposed algorithm, first, all binary versions of the QANA are assessed on different medical datasets with varied feature sizes, including Pima, HeartEW, Lymphography, SPECT Heart, PenglungEW, Parkinson, Colon, SRBCT, Leukemia, and Prostate tumor. The results show that the BQANA developed by the second approach is superior to other binary versions of the QANA to find the optimal feature subset from the medical datasets. Then, the BQANA was compared with nine well-known binary metaheuristic algorithms, and the results were statistically assessed using the Friedman test. The experimental and statistical results demonstrate that the proposed BQANA has merit for feature selection from medical datasets. |
| Author | Fatahi, Ali Nadimi-Shahraki, Mohammad H. Mirjalili, Seyedali Zamani, Hoda |
| Author_xml | – sequence: 1 givenname: Mohammad H. orcidid: 0000-0002-0135-1115 surname: Nadimi-Shahraki fullname: Nadimi-Shahraki, Mohammad H. – sequence: 2 givenname: Ali orcidid: 0000-0002-7779-3470 surname: Fatahi fullname: Fatahi, Ali – sequence: 3 givenname: Hoda orcidid: 0000-0003-0444-4509 surname: Zamani fullname: Zamani, Hoda – sequence: 4 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali |
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| Cites_doi | 10.1109/MCI.2006.329691 10.1007/s10462-021-10114-z 10.1007/s12065-021-00590-1 10.1038/89044 10.1016/j.neucom.2016.03.101 10.1016/j.knosys.2020.106560 10.1007/978-0-387-47509-7_7 10.1002/cpe.6310 10.1016/j.knosys.2022.108743 10.1109/ICPR.2014.251 10.3390/app9091776 10.1073/pnas.96.12.6745 10.3390/electronics11050831 10.1016/j.eswa.2014.01.011 10.1016/j.eswa.2021.116368 10.1016/j.cmpb.2013.10.007 10.3390/su14010541 10.1016/j.compbiomed.2019.103375 10.1109/ICECCT.2017.8118028 10.1016/j.eswa.2022.116895 10.1111/coin.12397 10.1016/j.swevo.2012.09.002 10.1007/s11831-021-09589-4 10.1061/(ASCE)CP.1943-5487.0000561 10.1007/s11047-009-9175-3 10.1016/j.eswa.2018.09.015 10.1016/j.swevo.2011.02.002 10.1016/j.aci.2018.12.004 10.1109/IDAP.2018.8620828 10.1016/j.engappai.2021.104314 10.1016/j.renene.2022.05.164 10.1007/s00521-017-2837-7 10.1007/978-3-540-70706-6_24 10.3390/pr9122276 10.1109/TKDE.2005.66 10.1007/s10916-017-0703-x 10.1007/978-3-030-10674-4 10.3390/e23091189 10.1080/09540091.2020.1741515 10.1016/j.asoc.2019.105576 10.1016/j.eswa.2014.08.014 10.1109/CISIS.2010.116 10.1016/j.eswa.2008.08.022 10.1007/s00500-016-2106-1 10.1016/j.eswa.2014.11.038 10.1016/j.compbiomed.2021.105027 10.1016/j.jnca.2011.01.002 10.3390/math10081303 10.1109/MIPRO.2015.7160458 10.1016/j.neucom.2017.01.126 10.3390/s90705339 10.3390/math10030361 10.1016/j.asej.2022.101809 10.1111/exsy.12553 10.1016/j.sigpro.2012.10.022 10.1126/science.286.5439.531 10.1109/TPWRS.2002.1007886 10.1080/13102818.2017.1364977 10.1016/j.compbiomed.2022.105858 10.1007/s10922-022-09653-9 10.1109/INISTA.2016.7571853 10.3390/electronics8101130 10.3390/electronics10141633 10.3390/e23121637 10.3390/s22030855 10.1016/S0142-0615(01)00067-9 10.1007/s10489-021-02233-5 10.3390/su131810419 10.1142/S0219622020500546 10.1109/SIBGRAPI.2012.47 10.1109/TEVC.2018.2869405 10.1016/j.enconman.2018.05.062 10.1016/j.procs.2013.10.003 10.1145/3321707.3321713 10.1109/CEC.2013.6557555 10.1016/j.cor.2005.11.017 10.1186/s40537-019-0241-0 10.1016/j.knosys.2014.03.015 10.1016/j.neucom.2015.06.083 10.1016/j.asoc.2019.03.002 10.1155/2020/6502807 10.1007/s00521-015-1920-1 10.1016/j.asoc.2019.105583 10.1007/s00202-021-01441-z 10.1016/j.patrec.2017.10.002 10.1007/s10710-019-09358-0 10.1016/j.cma.2022.114616 10.1007/978-981-15-3290-0_19 10.1007/s11227-021-04108-5 10.1109/ACCESS.2021.3097006 10.3390/en14123459 10.1002/dac.4434 10.3390/a14070200 10.1007/s12652-018-1031-9 10.1155/2022/6627409 |
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| References | Radpour (ref_65) 2022; 5 ref_94 Abido (ref_61) 2002; 24 Gharehchopogh (ref_9) 2021; 15 Remeseiro (ref_1) 2019; 112 ref_10 Zamani (ref_101) 2022; 148 ref_96 Kar (ref_112) 2015; 42 ref_17 Gharehchopogh (ref_43) 2021; 33 Chou (ref_57) 2016; 30 Renuka (ref_93) 2015; 22 Amiri (ref_19) 2011; 34 Aslan (ref_97) 2019; 82 Chen (ref_91) 2013; 93 Abualigah (ref_66) 2022; 192 Elsheikh (ref_47) 2022; 29 Fayyad (ref_5) 1996; 17 Ayar (ref_15) 2022; 78 Inbarani (ref_12) 2014; 113 ref_23 Too (ref_88) 2020; 32 ref_20 Kaur (ref_107) 2020; 18 ref_29 ref_27 Lin (ref_99) 2017; 21 Mostafa (ref_68) 2022; 246 Chakraborty (ref_49) 2022; 55 Mafarja (ref_70) 2019; 117 ref_78 ref_76 Rashedi (ref_77) 2010; 9 Bakirtzis (ref_62) 2002; 17 ref_74 Jordehi (ref_81) 2019; 78 Mirjalili (ref_98) 2013; 9 Bharti (ref_16) 2015; 42 Sindhu (ref_71) 2017; 28 (ref_50) 2022; 2022 Aghdam (ref_92) 2016; 18 Tran (ref_103) 2018; 23 Mohammadzadeh (ref_25) 2021; 20 ref_83 Papa (ref_95) 2017; 100 ref_80 Izakian (ref_53) 2009; 9 Zamani (ref_75) 2022; 198 Mohammadzadeh (ref_22) 2021; 37 Oliva (ref_39) 2018; 171 Zamani (ref_11) 2016; 14 ref_85 ref_84 Zamani (ref_40) 2019; 85 Farhat (ref_60) 2021; 9 Guo (ref_79) 2020; 2020 ref_58 ref_56 ref_55 ref_54 ref_52 ref_51 ref_59 Aghdam (ref_90) 2009; 36 Dorigo (ref_89) 2006; 1 Sharda (ref_31) 2022; 13 Kalantari (ref_4) 2018; 276 Ibrahim (ref_8) 2019; 10 Mirjalili (ref_87) 2016; 27 Chen (ref_32) 2020; 37 ref_67 Chatterjee (ref_14) 2022; 141 ref_64 ref_63 Zhang (ref_24) 2014; 64 Golub (ref_111) 1999; 286 Dhiman (ref_72) 2021; 211 Derrac (ref_105) 2011; 1 Guyon (ref_6) 2003; 3 Emary (ref_86) 2016; 213 Emary (ref_69) 2016; 172 ref_36 ref_34 ref_33 Liao (ref_82) 2007; 34 Liu (ref_30) 2005; 17 Zamani (ref_48) 2022; 392 ref_38 ref_37 Zamani (ref_73) 2021; 104 Esfandiari (ref_2) 2014; 41 Abusamra (ref_26) 2013; 23 ref_104 ref_106 ref_108 Polat (ref_13) 2017; 41 ref_46 ref_45 ref_44 ref_100 ref_42 Khan (ref_110) 2001; 7 ref_41 ref_102 Hashemi (ref_3) 2018; 32 Qiu (ref_7) 2019; 20 Naseri (ref_18) 2022; 30 Agrawal (ref_35) 2022; 52 Alon (ref_109) 1999; 96 Ghaffari (ref_21) 2020; 33 Tadist (ref_28) 2019; 6 |
| References_xml | – ident: ref_74 – volume: 1 start-page: 28 year: 2006 ident: ref_89 article-title: Ant colony optimization publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2006.329691 – volume: 55 start-page: 4605 year: 2022 ident: ref_49 article-title: A novel improved whale optimization algorithm to solve numerical optimization and real-world applications publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-021-10114-z – volume: 15 start-page: 1777 year: 2021 ident: ref_9 article-title: Chaotic vortex search algorithm: Metaheuristic algorithm for feature selection publication-title: Evol. Intell. doi: 10.1007/s12065-021-00590-1 – ident: ref_100 – volume: 7 start-page: 673 year: 2001 ident: ref_110 article-title: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks publication-title: Nat. Med. doi: 10.1038/89044 – volume: 213 start-page: 54 year: 2016 ident: ref_86 article-title: Binary ant lion approaches for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.03.101 – volume: 211 start-page: 106560 year: 2021 ident: ref_72 article-title: BEPO: A novel binary emperor penguin optimizer for automatic feature selection publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2020.106560 – ident: ref_27 doi: 10.1007/978-0-387-47509-7_7 – volume: 5 start-page: 90 year: 2022 ident: ref_65 article-title: A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease publication-title: Sak. Univ. J. Comput. Inf. Sci. – ident: ref_108 – volume: 33 start-page: e6310 year: 2021 ident: ref_43 article-title: A modified farmland fertility algorithm for solving constrained engineering problems publication-title: Concurr. Comput. Pract. Exp. doi: 10.1002/cpe.6310 – volume: 22 start-page: 22 year: 2015 ident: ref_93 article-title: Improving Email spam classification using ant colony optimization algorithm publication-title: Int. J. Comput. Appl. – volume: 246 start-page: 108743 year: 2022 ident: ref_68 article-title: Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2022.108743 – ident: ref_36 doi: 10.1109/ICPR.2014.251 – ident: ref_52 doi: 10.3390/app9091776 – volume: 96 start-page: 6745 year: 1999 ident: ref_109 article-title: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.96.12.6745 – ident: ref_63 doi: 10.3390/electronics11050831 – volume: 41 start-page: 4434 year: 2014 ident: ref_2 article-title: Knowledge discovery in medicine: Current issue and future trend publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.01.011 – ident: ref_10 – volume: 192 start-page: 116368 year: 2022 ident: ref_66 article-title: Chaotic binary group search optimizer for feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116368 – ident: ref_83 – volume: 113 start-page: 175 year: 2014 ident: ref_12 article-title: Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2013.10.007 – ident: ref_54 doi: 10.3390/su14010541 – volume: 112 start-page: 103375 year: 2019 ident: ref_1 article-title: A review of feature selection methods in medical applications publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.103375 – ident: ref_64 doi: 10.1109/ICECCT.2017.8118028 – volume: 198 start-page: 116895 year: 2022 ident: ref_75 article-title: DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.116895 – volume: 37 start-page: 176 year: 2021 ident: ref_22 article-title: A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection publication-title: Comput. Intell. doi: 10.1111/coin.12397 – volume: 3 start-page: 1157 year: 2003 ident: ref_6 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – volume: 9 start-page: 1 year: 2013 ident: ref_98 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: 29 start-page: 695 year: 2022 ident: ref_47 article-title: Advanced metaheuristic techniques for mechanical design problems publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-021-09589-4 – volume: 30 start-page: 04016007 year: 2016 ident: ref_57 article-title: Nature-inspired metaheuristic regression system: Programming and implementation for civil engineering applications publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000561 – volume: 9 start-page: 727 year: 2010 ident: ref_77 article-title: BGSA: Binary gravitational search algorithm publication-title: Nat. Comput. doi: 10.1007/s11047-009-9175-3 – volume: 117 start-page: 267 year: 2019 ident: ref_70 article-title: Binary grasshopper optimisation algorithm approaches for feature selection problems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.09.015 – volume: 1 start-page: 3 year: 2011 ident: ref_105 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 – volume: 18 start-page: 90 year: 2020 ident: ref_107 article-title: Predictive modelling and analytics for diabetes using a machine learning approach publication-title: Appl. Comput. Inform. doi: 10.1016/j.aci.2018.12.004 – volume: 17 start-page: 37 year: 1996 ident: ref_5 article-title: From data mining to knowledge discovery in databases publication-title: AI Mag. – ident: ref_106 – ident: ref_94 doi: 10.1109/IDAP.2018.8620828 – volume: 14 start-page: 1243 year: 2016 ident: ref_11 article-title: Feature selection based on whale optimization algorithm for diseases diagnosis publication-title: Int. J. Comput. Sci. Inf. Secur. – volume: 104 start-page: 104314 year: 2021 ident: ref_73 article-title: QANA: Quantum-based avian navigation optimizer algorithm publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2021.104314 – ident: ref_44 doi: 10.1016/j.renene.2022.05.164 – volume: 28 start-page: 2947 year: 2017 ident: ref_71 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 – ident: ref_84 doi: 10.1007/978-3-540-70706-6_24 – ident: ref_37 doi: 10.3390/pr9122276 – volume: 17 start-page: 491 year: 2005 ident: ref_30 article-title: Toward integrating feature selection algorithms for classification and clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2005.66 – volume: 41 start-page: 55 year: 2017 ident: ref_13 article-title: Diagnosis of chronic kidney disease based on support vector machine by feature selection methods publication-title: J. Med. Syst. doi: 10.1007/s10916-017-0703-x – ident: ref_17 doi: 10.1007/978-3-030-10674-4 – ident: ref_67 doi: 10.3390/e23091189 – volume: 32 start-page: 406 year: 2020 ident: ref_88 article-title: Binary atom search optimisation approaches for feature selection publication-title: Connect. Sci. doi: 10.1080/09540091.2020.1741515 – volume: 82 start-page: 105576 year: 2019 ident: ref_97 article-title: JayaX: Jaya algorithm with xor operator for binary optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105576 – volume: 42 start-page: 612 year: 2015 ident: ref_112 article-title: Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.08.014 – ident: ref_23 doi: 10.1109/CISIS.2010.116 – volume: 36 start-page: 6843 year: 2009 ident: ref_90 article-title: Text feature selection using ant colony optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.08.022 – volume: 21 start-page: 5103 year: 2017 ident: ref_99 article-title: A binary PSO approach to mine high-utility itemsets publication-title: Soft Comput. doi: 10.1007/s00500-016-2106-1 – volume: 42 start-page: 3105 year: 2015 ident: ref_16 article-title: Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.11.038 – volume: 141 start-page: 105027 year: 2022 ident: ref_14 article-title: Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.105027 – volume: 34 start-page: 1184 year: 2011 ident: ref_19 article-title: Mutual information-based feature selection for intrusion detection systems publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2011.01.002 – ident: ref_55 doi: 10.3390/math10081303 – ident: ref_33 doi: 10.1109/MIPRO.2015.7160458 – volume: 276 start-page: 2 year: 2018 ident: ref_4 article-title: Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.126 – volume: 9 start-page: 5339 year: 2009 ident: ref_53 article-title: Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems publication-title: Sensors doi: 10.3390/s90705339 – ident: ref_59 doi: 10.3390/math10030361 – volume: 13 start-page: 101809 year: 2022 ident: ref_31 article-title: A hybrid machine learning technique for feature optimization in object-based classification of debris-covered glaciers publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2022.101809 – volume: 37 start-page: e12553 year: 2020 ident: ref_32 article-title: Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results publication-title: Expert Syst. doi: 10.1111/exsy.12553 – volume: 93 start-page: 1566 year: 2013 ident: ref_91 article-title: Efficient ant colony optimization for image feature selection publication-title: Signal Process. doi: 10.1016/j.sigpro.2012.10.022 – volume: 286 start-page: 531 year: 1999 ident: ref_111 article-title: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring publication-title: Science doi: 10.1126/science.286.5439.531 – ident: ref_58 – volume: 17 start-page: 229 year: 2002 ident: ref_62 article-title: Optimal power flow by enhanced genetic algorithm publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2002.1007886 – volume: 32 start-page: 10 year: 2018 ident: ref_3 article-title: Intelligent mining of large-scale bio-data: Bioinformatics applications publication-title: Biotechnol. Biotechnol. Equip. doi: 10.1080/13102818.2017.1364977 – volume: 148 start-page: 105858 year: 2022 ident: ref_101 article-title: Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105858 – volume: 30 start-page: 40 year: 2022 ident: ref_18 article-title: A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems publication-title: J. Netw. Syst. Manag. doi: 10.1007/s10922-022-09653-9 – ident: ref_102 doi: 10.1109/INISTA.2016.7571853 – ident: ref_80 doi: 10.3390/electronics8101130 – ident: ref_20 doi: 10.3390/electronics10141633 – ident: ref_46 doi: 10.3390/e23121637 – ident: ref_41 doi: 10.3390/s22030855 – volume: 24 start-page: 563 year: 2002 ident: ref_61 article-title: Optimal power flow using particle swarm optimization publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/S0142-0615(01)00067-9 – volume: 52 start-page: 81 year: 2022 ident: ref_35 article-title: S-shaped and v-shaped gaining-sharing knowledge-based algorithm for feature selection publication-title: Appl. Intell. doi: 10.1007/s10489-021-02233-5 – ident: ref_42 doi: 10.3390/su131810419 – volume: 20 start-page: 469 year: 2021 ident: ref_25 article-title: Feature selection with binary symbiotic organisms search algorithm for email spam detection publication-title: Int. J. Inf. Technol. Decis. Mak. doi: 10.1142/S0219622020500546 – ident: ref_85 doi: 10.1109/SIBGRAPI.2012.47 – volume: 23 start-page: 473 year: 2018 ident: ref_103 article-title: Variable-length particle swarm optimization for feature selection on high-dimensional classification publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2018.2869405 – volume: 171 start-page: 1843 year: 2018 ident: ref_39 article-title: Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2018.05.062 – volume: 23 start-page: 5 year: 2013 ident: ref_26 article-title: A comparative study of feature selection and classification methods for gene expression data of glioma publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2013.10.003 – ident: ref_38 doi: 10.1145/3321707.3321713 – ident: ref_76 – ident: ref_104 doi: 10.1109/CEC.2013.6557555 – volume: 34 start-page: 3099 year: 2007 ident: ref_82 article-title: A discrete version of particle swarm optimization for flowshop scheduling problems publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2005.11.017 – volume: 6 start-page: 79 year: 2019 ident: ref_28 article-title: Feature selection methods and genomic big data: A systematic review publication-title: J. Big Data doi: 10.1186/s40537-019-0241-0 – ident: ref_34 – volume: 64 start-page: 22 year: 2014 ident: ref_24 article-title: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2014.03.015 – volume: 172 start-page: 371 year: 2016 ident: ref_69 article-title: Binary grey wolf optimization approaches for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.083 – volume: 78 start-page: 465 year: 2019 ident: ref_81 article-title: Binary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homes publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.03.002 – volume: 2020 start-page: 6502807 year: 2020 ident: ref_79 article-title: Z-shaped transfer functions for binary particle swarm optimization algorithm publication-title: Comput. Intell. Neurosci. doi: 10.1155/2020/6502807 – volume: 27 start-page: 1053 year: 2016 ident: ref_87 article-title: Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems publication-title: Neural Comput. Appl. doi: 10.1007/s00521-015-1920-1 – volume: 85 start-page: 105583 year: 2019 ident: ref_40 article-title: CCSA: Conscious neighborhood-based crow search algorithm for solving global optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105583 – ident: ref_96 – ident: ref_45 doi: 10.1007/s00202-021-01441-z – volume: 100 start-page: 59 year: 2017 ident: ref_95 article-title: A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes publication-title: Pattern Recog. Lett. doi: 10.1016/j.patrec.2017.10.002 – volume: 20 start-page: 503 year: 2019 ident: ref_7 article-title: A novel multi-swarm particle swarm optimization for feature selection publication-title: Genet. Program. Evolvable Mach. doi: 10.1007/s10710-019-09358-0 – ident: ref_29 – volume: 18 start-page: 420 year: 2016 ident: ref_92 article-title: Feature selection for intrusion detection system using ant colony optimization publication-title: Int. J. Netw. Secur. – volume: 392 start-page: 114616 year: 2022 ident: ref_48 article-title: Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2022.114616 – ident: ref_78 doi: 10.1007/978-981-15-3290-0_19 – volume: 78 start-page: 5856 year: 2022 ident: ref_15 article-title: Chaotic-based divide-and-conquer feature selection method and its application in cardiac arrhythmia classification publication-title: J. Supercomput. doi: 10.1007/s11227-021-04108-5 – volume: 9 start-page: 100911 year: 2021 ident: ref_60 article-title: Optimal power flow solution based on jellyfish search optimization considering uncertainty of renewable energy sources publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3097006 – ident: ref_56 doi: 10.3390/en14123459 – volume: 33 start-page: e4434 year: 2020 ident: ref_21 article-title: A wrapper-based feature selection for improving performance of intrusion detection systems publication-title: Int. J. Commun. Syst. doi: 10.1002/dac.4434 – ident: ref_51 doi: 10.3390/a14070200 – volume: 10 start-page: 3155 year: 2019 ident: ref_8 article-title: Improved salp swarm algorithm based on particle swarm optimization for feature selection publication-title: J. Ambient. Intell. Humaniz. Comput. doi: 10.1007/s12652-018-1031-9 – volume: 2022 start-page: 6627409 year: 2022 ident: ref_50 article-title: Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems publication-title: Complexity doi: 10.1155/2022/6627409 |
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| SubjectTerms | Accuracy Algorithms binary metaheuristic algorithms Boolean Classification Colon Costs Datasets Feature selection Heuristic methods Intrusion detection systems Leukemia medical datasets Medical research Methods Navigation optimization Optimization algorithms swarm intelligence algorithms Transfer functions |
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| Title | Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data |
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