{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T20:59:12Z","timestamp":1770497952198,"version":"3.49.0"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Iran J Comput Sci"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s42044-024-00174-z","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T13:03:17Z","timestamp":1707397397000},"page":"279-309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An efficient high-dimensional gene selection approach based on the Binary Horse Herd Optimization Algorithm for biologicaldata classification"],"prefix":"10.1007","volume":"7","author":[{"given":"Niloufar","family":"Mehrabi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sayed Pedram","family":"Haeri Boroujeni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elnaz","family":"Pashaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"174_CR1","doi-asserted-by":"publisher","unstructured":"Chizi, B., Rokach, L., Maimon, O.: A survey of feature selection techniques. In Encyclopedia of Data Warehousing and Mining, Second Edition, pp. 1888\u20131895. IGI Global (2009). https:\/\/doi.org\/10.4018\/978-1-60566-010-3.ch289","DOI":"10.4018\/978-1-60566-010-3.ch289"},{"issue":"4","key":"174_CR2","doi-asserted-by":"publisher","first-page":"btad110","DOI":"10.1093\/bioinformatics\/btad110","volume":"39","author":"M Shaffer","year":"2023","unstructured":"Shaffer, M., Borton, M.A., Bolduc, B., Faria, J.P., Flynn, R.M., Ghadermazi, P., Wrighton, K.C., et al.: kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase. Bioinformatics 39(4), btad110 (2023). https:\/\/doi.org\/10.1093\/bioinformatics\/btad110","journal-title":"Bioinformatics"},{"key":"174_CR3","doi-asserted-by":"publisher","first-page":"106337","DOI":"10.1016\/j.asoc.2020.106337","volume":"93","author":"G Wei","year":"2020","unstructured":"Wei, G., Zhao, J., Feng, Y., He, A., Yu, J.: A novel hybrid feature selection method based on dynamic feature importance. Appl. Soft Comput. 93, 106337 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106337","journal-title":"Appl. Soft Comput."},{"key":"174_CR4","doi-asserted-by":"publisher","first-page":"106131","DOI":"10.1016\/j.knosys.2020.106131","volume":"203","author":"AI Hammouri","year":"2020","unstructured":"Hammouri, A.I., Mafarja, M., Al-Betar, M.A., Awadallah, M.A., Abu-Doush, I.: An improved dragonfly algorithm for feature selection. Knowl.-Based Syst. 203, 106131 (2020). https:\/\/doi.org\/10.1016\/j.knosys.2020.106131","journal-title":"Knowl.-Based Syst."},{"key":"174_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3067597","author":"Y Meraihi","year":"2021","unstructured":"Meraihi, Y., Gabis, A.B., Mirjalili, S., Ramdane-Cherif, A.: Grasshopper optimization algorithm: theory, variants, and applications. IEEE Access (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3067597","journal-title":"IEEE Access"},{"key":"174_CR6","doi-asserted-by":"publisher","unstructured":"Mehrabi, N., Pashaei, E. Application of horse herd optimization algorithm for medical problems. In: 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1\u20136. IEEE (2021). https:\/\/doi.org\/10.1109\/INISTA52262.2021.9548366","DOI":"10.1109\/INISTA52262.2021.9548366"},{"key":"174_CR7","doi-asserted-by":"publisher","unstructured":"Boroujeni, S.P.H., Pashaei, E.: Data clustering using chimp optimization algorithm. In: 2021 11th International Conference on Computer Engineering and Knowledge(ICCKE), pp. 296\u2013301 IEEE (2021). https:\/\/doi.org\/10.1109\/ICCKE54056.2021.9721483","DOI":"10.1109\/ICCKE54056.2021.9721483"},{"key":"174_CR8","doi-asserted-by":"publisher","unstructured":"Mehrabi, N., Boroujeni, S.P.H.: Age estimation based on facial images using hybrid features and particle swarm optimization. In: 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), pp. 412\u2013418. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCKE54056.2021.9721496","DOI":"10.1109\/ICCKE54056.2021.9721496"},{"issue":"2","key":"174_CR9","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/electronics10020101","volume":"10","author":"L Abualigah","year":"2021","unstructured":"Abualigah, L., Gandomi, A.H., Elaziz, M.A., Hamad, H.A., Omari, M., Alshinwan, M., Khasawneh, A.M.: Advances in meta-heuristic optimization algorithms in big data text clustering. Electronics 10(2), 101 (2021). https:\/\/doi.org\/10.3390\/electronics10020101","journal-title":"Electronics"},{"key":"174_CR10","doi-asserted-by":"publisher","first-page":"121962","DOI":"10.1016\/j.eswa.2023.121962","volume":"238","author":"SPH Boroujeni","year":"2024","unstructured":"Boroujeni, S.P.H., Razi, A.: IC-GAN: an improved conditional generative adversarial network for RGB-to-IR image translation with applications to forest fire monitoring. Expert Syst. Appl. 238, 121962 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121962","journal-title":"Expert Syst. Appl."},{"key":"174_CR11","doi-asserted-by":"publisher","first-page":"610","DOI":"10.31590\/ejosat.802810","volume":"21","author":"E Erdem","year":"2021","unstructured":"Erdem, E., Bozkurt, F.: A comparison of various supervised machine learning techniques for prostate cancer prediction. Avrupa Bilim ve Teknol. Derg. 21, 610\u2013620 (2021). https:\/\/doi.org\/10.31590\/ejosat.802810","journal-title":"Avrupa Bilim ve Teknol. Derg."},{"key":"174_CR12","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.patcog.2018.12.020","volume":"88","author":"S Kashef","year":"2019","unstructured":"Kashef, S., Nezamabadi-pour, H.: A label-specific multi-label feature selection algorithm based on the Pareto dominance concept. Pattern Recogn. 88, 654\u2013667 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2018.12.020","journal-title":"Pattern Recogn."},{"key":"174_CR13","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.neucom.2019.01.017","volume":"333","author":"J Gonz\u00e1lez","year":"2019","unstructured":"Gonz\u00e1lez, J., Ortega, J., Damas, M., Mart\u00edn-Smith, P., Gan, J.Q.: A new multi-objective wrapper method for feature selection\u2013accuracy and stability analysis for BCI. Neurocomputing 333, 407\u2013418 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.01.017","journal-title":"Neurocomputing"},{"key":"174_CR14","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patcog.2019.06.003","volume":"95","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Luo, Z., Li, C., Zhou, C., Li, S.: Manifold regularized discriminative feature selection for multi-label learning. Pattern Recogn. 95, 136\u2013150 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2019.06.003","journal-title":"Pattern Recogn."},{"key":"174_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3006197","author":"SK Prabhakar","year":"2020","unstructured":"Prabhakar, S.K., Lee, S.W.: Transformation based tri-level feature selection approach using wavelets and swarm computing for prostate cancer classification. IEEE Access (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3006197","journal-title":"IEEE Access"},{"key":"174_CR16","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty788","author":"TT Le","year":"2019","unstructured":"Le, T.T., Urbanowicz, R.J., Moore, J.H., McKinney, B.A.: STatistical Inference Relief (STIR) feature selection. Bioinformatics (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/bty788","journal-title":"Bioinformatics"},{"key":"174_CR17","doi-asserted-by":"publisher","first-page":"114765","DOI":"10.1016\/j.eswa.2021.114765","volume":"174","author":"EO Omuya","year":"2021","unstructured":"Omuya, E.O., Okeyo, G.O., Kimwele, M.W.: Feature selection for classification using principal component analysis and information gain. Expert Syst. Appl. 174, 114765 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.114765","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"174_CR18","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.jksuci.2018.05.010","volume":"32","author":"S Bahassine","year":"2020","unstructured":"Bahassine, S., Madani, A., Al-Sarem, M., Kissi, M.: Feature selection using an improved Chi-square for Arabic text classification. J. King Saud Univ.-Comput. Inf. Sci. 32(2), 225\u2013231 (2020). https:\/\/doi.org\/10.1016\/j.jksuci.2018.05.010","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"174_CR19","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.asoc.2017.03.002","volume":"56","author":"E Pashaei","year":"2017","unstructured":"Pashaei, E., Aydin, N.: Binary black hole algorithm for feature selection and classification on biological data. Appl. Soft Comput. 56, 94\u2013106 (2017). https:\/\/doi.org\/10.1016\/j.asoc.2017.03.002","journal-title":"Appl. Soft Comput."},{"key":"174_CR20","doi-asserted-by":"publisher","first-page":"107470","DOI":"10.1016\/j.patcog.2020.107470","volume":"107","author":"RCT de Souza","year":"2020","unstructured":"de Souza, R.C.T., de Macedo, C.A., dos Santos Coelho, L., Pierezan, J., Mariani, V.C.: Binary coyote optimization algorithm for feature selection. Pattern Recogn. 107, 107470 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107470","journal-title":"Pattern Recogn."},{"key":"174_CR21","doi-asserted-by":"publisher","unstructured":"Sarlak, A., Razi, A., Chen, X., Amin, R.: Diversity maximized scheduling in roadside units for traffic monitoring applications. In: 2023 IEEE 48th Conference on Local Computer Networks (LCN), pp. 1\u20134. IEEE (2023). https:\/\/doi.org\/10.1109\/LCN58197.2023.10223373","DOI":"10.1109\/LCN58197.2023.10223373"},{"issue":"3","key":"174_CR22","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.jcde.2019.02.002","volume":"6","author":"S Mostafa Bozorgi","year":"2019","unstructured":"Mostafa Bozorgi, S., Yazdani, S.: IWOA: an improved whale optimization algorithm for optimization problems. J. Comput. Design Eng. 6(3), 243\u2013259 (2019). https:\/\/doi.org\/10.1016\/j.jcde.2019.02.002","journal-title":"J. Comput. Design Eng."},{"key":"174_CR23","doi-asserted-by":"publisher","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe, M., Mosavi, M.R.: Chimp optimization algorithm. Expert Syst. Appl. 149, 113338 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113338","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"174_CR24","first-page":"177","volume":"11","author":"G Azizyan","year":"2019","unstructured":"Azizyan, G., Miarnaeimi, F., Rashki, M., Shabakhty, N.: Flying Squirrel Optimizer (FSO): a novel SI-based optimization algorithm for engineering problems. Iran. J. Optimiz. 11(2), 177\u2013205 (2019).","journal-title":"Iran. J. Optimiz."},{"key":"174_CR25","doi-asserted-by":"publisher","unstructured":"Pierezan, J., Dos Santos Coelho, L.: Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: 2018 IEEE Congress on Evolutionary Computation, CEC 2018\u2014Proceedings (2018). https:\/\/doi.org\/10.1109\/CEC.2018.8477769","DOI":"10.1109\/CEC.2018.8477769"},{"key":"174_CR26","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849\u2013872 (2019). https:\/\/doi.org\/10.1016\/j.future.2019.02.028","journal-title":"Futur. Gener. Comput. Syst."},{"key":"174_CR27","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s10489-017-1019-8","volume":"48","author":"SZ Mirjalili","year":"2018","unstructured":"Mirjalili, S.Z., Mirjalili, S., Saremi, S., Faris, H., Aljarah, I.: Grasshopper optimization algorithm for multi-objective optimization problems. Appl. Intell. 48, 805\u2013820 (2018). https:\/\/doi.org\/10.1007\/s10489-017-1019-8","journal-title":"Appl. Intell."},{"key":"174_CR28","doi-asserted-by":"publisher","unstructured":"Pashaei, E., Pashaei, E.: Gene selection using intelligent dynamic genetic algorithm and random forest. In: ELECO 2019\u201411th International Conference on Electrical and Electronics Engineering (2019). https:\/\/doi.org\/10.23919\/ELECO47770.2019.8990557","DOI":"10.23919\/ELECO47770.2019.8990557"},{"key":"174_CR29","doi-asserted-by":"publisher","first-page":"106711","DOI":"10.1016\/j.knosys.2020.106711","volume":"213","author":"F MiarNaeimi","year":"2021","unstructured":"MiarNaeimi, F., Azizyan, G., Rashki, M.: Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowl.-Based Syst. 213, 106711 (2021). https:\/\/doi.org\/10.1016\/j.knosys.2020.106711","journal-title":"Knowl.-Based Syst."},{"key":"174_CR30","doi-asserted-by":"publisher","unstructured":"Boroujeni, S.P.H., Razi, A., Khoshdel, S., Afghah, F., Coen, J.L., ONeill, L., Vamvoudakis, K.G. et al.: A Comprehensive Survey of Research Towards AI-Enabled Unmanned Aerial Systems in Pre-, Active-, and Post-Wildfire Management. Springer, New York (2024). https:\/\/doi.org\/10.48550\/arXiv.2401.02456","DOI":"10.48550\/arXiv.2401.02456"},{"key":"174_CR31","first-page":"789","volume":"2003","author":"SM McDonnell","year":"2003","unstructured":"McDonnell, S.M., Poulin, A.: The equid ethogram: a practical field guide to horse behavior\u2014Sue M. McDonnell. Appl. Anim. Behav. Sci. 2003, 789 (2003)","journal-title":"Appl. Anim. Behav. Sci."},{"key":"174_CR32","unstructured":"Levine, M.A.: Domestication and early history of the horse. In: The Domestic Horse: The Evolution, Development, and Management of its Behaviour, pp. 5\u201322 Springer, New York (2005)"},{"key":"174_CR33","doi-asserted-by":"publisher","first-page":"97890","DOI":"10.1109\/ACCESS.2020.2996611","volume":"8","author":"KK Ghosh","year":"2020","unstructured":"Ghosh, K.K., Singh, P.K., Hong, J., Geem, Z.W., Sarkar, R.: Binary social mimic optimization algorithm with x-shaped transfer function for feature selection. IEEE Access 8, 97890\u201397906 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2996611","journal-title":"IEEE Access"},{"issue":"8","key":"174_CR34","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226\u20131238 (2005). https:\/\/doi.org\/10.1109\/TPAMI.2005.159","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"174_CR35","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence","author":"JH Holland","year":"1992","unstructured":"Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press, Cambridge (1992)"},{"key":"174_CR36","doi-asserted-by":"publisher","unstructured":"Van Laarhoven, P.J., Aarts, E.H., van Laarhoven, P.J., Aarts, E.H.: Simulated Annealing, pp. 7\u201315. Springer, Netherlands (1987). https:\/\/doi.org\/10.1007\/978-94-015-7744-1_2","DOI":"10.1007\/978-94-015-7744-1_2"},{"issue":"1\u20132","key":"174_CR37","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10710-019-09361-5","volume":"21","author":"L Araujo","year":"2020","unstructured":"Araujo, L.: Genetic programming for natural language processing. Genet. Program Evolvable Mach. 21(1\u20132), 11\u201332 (2020). https:\/\/doi.org\/10.1007\/s10710-019-09361-5","journal-title":"Genet. Program Evolvable Mach."},{"key":"174_CR38","doi-asserted-by":"publisher","unstructured":"Knowles, J.D., Corne, D.W.: M-PAES: a memetic algorithm for multiobjective optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), vol. 1, pp. 325\u2013332. IEEE (2000). https:\/\/doi.org\/10.1109\/CEC.2000.870313","DOI":"10.1109\/CEC.2000.870313"},{"key":"174_CR39","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ins.2015.04.031","volume":"316","author":"RJ Kuo","year":"2015","unstructured":"Kuo, R.J., Zulvia, F.E.: The gradient evolution algorithm: a new metaheuristic. Inf. Sci. 316, 246\u2013265 (2015). https:\/\/doi.org\/10.1016\/j.ins.2015.04.031","journal-title":"Inf. Sci."},{"key":"174_CR40","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014). https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv. Eng. Softw."},{"issue":"2","key":"174_CR41","doi-asserted-by":"publisher","first-page":"551","DOI":"10.24200\/sci.2017.2417","volume":"24","author":"A Kaveh","year":"2017","unstructured":"Kaveh, A., Ilchi Ghazaan, M.: A new meta-heuristic algorithm: vibrating particles system. Sci. Iran. 24(2), 551\u2013566 (2017). https:\/\/doi.org\/10.24200\/sci.2017.2417","journal-title":"Sci. Iran."},{"key":"174_CR42","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.1007\/s13042-019-00931-8","volume":"10","author":"N Al-Madi","year":"2019","unstructured":"Al-Madi, N., Faris, H., Mirjalili, S.: Binary multi-verse optimization algorithm for global optimization and discrete problems. Int. J. Mach. Learn. Cybern. 10, 3445\u20133465 (2019). https:\/\/doi.org\/10.1007\/s13042-019-00931-8","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"174_CR43","doi-asserted-by":"publisher","unstructured":"Boroujeni, S.P.H., Pashaei, E.: A novel hybrid gene selection based on random forest approach and binary dragonfly algorithm. In: 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1\u20138. IEEE (2021). https:\/\/doi.org\/10.1109\/CCE53527.2021.9633105","DOI":"10.1109\/CCE53527.2021.9633105"},{"key":"174_CR44","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s11047-009-9175-3","volume":"9","author":"E Rashedi","year":"2010","unstructured":"Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9, 727\u2013745 (2010). https:\/\/doi.org\/10.1007\/s11047-009-9175-3","journal-title":"Nat. Comput."},{"key":"174_CR45","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s00366-016-0457-y","volume":"33","author":"H Varaee","year":"2017","unstructured":"Varaee, H., Ghasemi, M.R.: Engineering optimization based on ideal gas molecular movement algorithm. Eng. Comput. 33, 71\u201393 (2017). https:\/\/doi.org\/10.1007\/s00366-016-0457-y","journal-title":"Eng. Comput."},{"issue":"13","key":"174_CR46","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232\u20132248 (2009). https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf. Sci."},{"key":"174_CR47","doi-asserted-by":"publisher","first-page":"110011","DOI":"10.1016\/j.knosys.2022.110011","volume":"259","author":"M Dehghani","year":"2023","unstructured":"Dehghani, M., Montazeri, Z., Trojovsk\u00e1, E., Trojovsk\u00fd, P.: Coati Optimization Algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl.-Based Syst. 259, 110011 (2023). https:\/\/doi.org\/10.1016\/j.knosys.2022.110011","journal-title":"Knowl.-Based Syst."},{"key":"174_CR48","doi-asserted-by":"publisher","first-page":"107302","DOI":"10.1016\/j.asoc.2021.107302","volume":"106","author":"AD Li","year":"2021","unstructured":"Li, A.D., Xue, B., Zhang, M.: Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies. Appl. Soft Comput. 106, 107302 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107302","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"174_CR49","doi-asserted-by":"publisher","first-page":"697","DOI":"10.33889\/IJMEMS.2020.5.4.056","volume":"5","author":"OS Qasim","year":"2020","unstructured":"Qasim, O.S., Algamal, Z.Y.: Feature selection using different transfer functions for binary bat algorithm. Int. J. Math. Eng. Manage. Sci. 5(4), 697 (2020). https:\/\/doi.org\/10.33889\/IJMEMS.2020.5.4.056","journal-title":"Int. J. Math. Eng. Manage. Sci."},{"key":"174_CR50","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s13042-019-00996-5","volume":"11","author":"MA Tawhid","year":"2020","unstructured":"Tawhid, M.A., Ibrahim, A.M.: Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm. Int. J. Mach. Learn. Cybern. 11, 573\u2013602 (2020). https:\/\/doi.org\/10.1007\/s13042-019-00996-5","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"174_CR51","doi-asserted-by":"publisher","first-page":"3741","DOI":"10.1007\/s00366-020-01028-5","volume":"37","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Liu, R., Wang, X., Chen, H., Li, C.: Boosted binary Harris hawks optimizer and feature selection. Eng. Comput. 37, 3741\u20133770 (2021). https:\/\/doi.org\/10.1007\/s00366-020-01028-5","journal-title":"Eng. Comput."},{"key":"174_CR52","doi-asserted-by":"publisher","first-page":"106560","DOI":"10.1016\/j.knosys.2020.106560","volume":"211","author":"G Dhiman","year":"2021","unstructured":"Dhiman, G., Oliva, D., Kaur, A., Singh, K.K., Vimal, S., Sharma, A., Cengiz, K.: BEPO: A novel binary emperor penguin optimizer for automatic feature selection. Knowl.-Based Syst. 211, 106560 (2021). https:\/\/doi.org\/10.1016\/j.knosys.2020.106560","journal-title":"Knowl.-Based Syst."},{"key":"174_CR53","doi-asserted-by":"publisher","first-page":"105746","DOI":"10.1016\/j.knosys.2020.105746","volume":"195","author":"P Hu","year":"2020","unstructured":"Hu, P., Pan, J.S., Chu, S.C.: Improved binary grey wolf optimizer and its application for feature selection. Knowl.-Based Syst. 195, 105746 (2020). https:\/\/doi.org\/10.1016\/j.knosys.2020.105746","journal-title":"Knowl.-Based Syst."},{"key":"174_CR54","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2311.10675","author":"AAR Lori","year":"2023","unstructured":"Lori, A.A.R.: Optimal path planning for aerial load transportation in complex environments using PSO-improved artificial potential fields. arXiv (2023). https:\/\/doi.org\/10.48550\/arXiv.2311.10675","journal-title":"arXiv"},{"key":"174_CR55","doi-asserted-by":"publisher","unstructured":"Lee, K.Y., Park, J.B.: Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. In: 2006 IEEE PES power systems conference and exposition (pp. 188\u2013192). IEEE (2006). https:\/\/doi.org\/10.1109\/PSCE.2006.296295","DOI":"10.1109\/PSCE.2006.296295"},{"key":"174_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42044-023-00160-x","volume":"2023","author":"SP Haeri Boroujeni","year":"2023","unstructured":"Haeri Boroujeni, S.P., Pashaei, E.: A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems. Iran J. Comput. Sci. 2023, 1\u201337 (2023). https:\/\/doi.org\/10.1007\/s42044-023-00160-x","journal-title":"Iran J. Comput. Sci."},{"key":"174_CR57","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.knosys.2018.06.001","volume":"159","author":"G Dhiman","year":"2018","unstructured":"Dhiman, G., Kumar, V.: Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159, 20\u201350 (2018). https:\/\/doi.org\/10.1016\/j.knosys.2018.06.001","journal-title":"Knowl.-Based Syst."},{"issue":"02","key":"174_CR58","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"3","author":"C Ding","year":"2005","unstructured":"Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinf. Comput. Biol. 3(02), 185\u2013205 (2005). https:\/\/doi.org\/10.1142\/S0219720005001004","journal-title":"J. Bioinf. Comput. Biol."},{"key":"174_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2012.09.002","volume":"9","author":"S Mirjalili","year":"2013","unstructured":"Mirjalili, S., Lewis, A.: S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1\u201314 (2013). https:\/\/doi.org\/10.1016\/j.swevo.2012.09.002","journal-title":"Swarm Evol. Comput."},{"key":"174_CR60","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2018","unstructured":"Mafarja, M., Mirjalili, S.: Whale optimization approaches for wrapper feature selection. Appl. Soft Comput. 62, 441\u2013453 (2018). https:\/\/doi.org\/10.1016\/j.asoc.2017.11.006","journal-title":"Appl. Soft Comput."},{"key":"174_CR61","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.knosys.2017.04.004","volume":"126","author":"H Wang","year":"2017","unstructured":"Wang, H., Jing, X., Niu, B.: A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data. Knowl.-Based Syst. 126, 8\u201319 (2017). https:\/\/doi.org\/10.1016\/j.knosys.2017.04.004","journal-title":"Knowl.-Based Syst."},{"key":"174_CR62","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.asoc.2015.01.035","volume":"30","author":"V Bol\u00f3n-Canedo","year":"2015","unstructured":"Bol\u00f3n-Canedo, V., S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A.: Distributed feature selection: an application to microarray data classification. Appl. Soft Comput. 30, 136\u2013150 (2015). https:\/\/doi.org\/10.1016\/j.asoc.2015.01.035","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"174_CR63","doi-asserted-by":"publisher","first-page":"11515","DOI":"10.1016\/j.eswa.2011.03.028","volume":"38","author":"RN Khushaba","year":"2011","unstructured":"Khushaba, R.N., Al-Ani, A., Al-Jumaily, A.: Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl. 38(9), 11515\u201311526 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.03.028","journal-title":"Expert Syst. Appl."},{"key":"174_CR64","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1007\/s10489-018-1207-1","volume":"48","author":"OA Alomari","year":"2018","unstructured":"Alomari, O.A., Khader, A.T., Al-Betar, M.A., Awadallah, M.A.: A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm with \u03b2-hill climbing. Appl. Intell. 48, 4429\u20134447 (2018). https:\/\/doi.org\/10.1007\/s10489-018-1207-1","journal-title":"Appl. Intell."},{"issue":"1","key":"174_CR65","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1089\/cmb.2010.0064","volume":"19","author":"LY Chuang","year":"2012","unstructured":"Chuang, L.Y., Yang, C.H., Li, J.C., Yang, C.H.: A hybrid BPSO-CGA approach for gene selection and classification of microarray data. J. Comput. Biol. 19(1), 68\u201382 (2012). https:\/\/doi.org\/10.1089\/cmb.2010.0064","journal-title":"J. Comput. Biol."},{"issue":"12","key":"174_CR66","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1016\/j.patcog.2005.11.001","volume":"39","author":"R Ruiz","year":"2006","unstructured":"Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn. 39(12), 2383\u20132392 (2006). https:\/\/doi.org\/10.1016\/j.patcog.2005.11.001","journal-title":"Pattern Recogn."},{"key":"174_CR67","doi-asserted-by":"publisher","first-page":"104079","DOI":"10.1016\/j.engappai.2020.104079","volume":"97","author":"Z Sadeghian","year":"2021","unstructured":"Sadeghian, Z., Akbari, E., Nematzadeh, H.: A hybrid feature selection method based on information theory and binary butterfly optimization algorithm. Eng. Appl. Artif. Intell. 97, 104079 (2021). https:\/\/doi.org\/10.1016\/j.engappai.2020.104079","journal-title":"Eng. Appl. Artif. Intell."},{"key":"174_CR68","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.asoc.2017.09.038","volume":"62","author":"I Jain","year":"2018","unstructured":"Jain, I., Jain, V.K., Jain, R.: Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Appl. Soft Comput. 62, 203\u2013215 (2018). https:\/\/doi.org\/10.1016\/j.asoc.2017.09.038","journal-title":"Appl. Soft Comput."},{"key":"174_CR69","doi-asserted-by":"publisher","first-page":"114242","DOI":"10.1016\/j.ab.2021.114242","volume":"627","author":"E Pashaei","year":"2021","unstructured":"Pashaei, E., Pashaei, E.: Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data. Anal. Biochem. 627, 114242 (2021). https:\/\/doi.org\/10.1016\/j.ab.2021.114242","journal-title":"Anal. Biochem."},{"issue":"1","key":"174_CR70","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.compbiolchem.2007.09.005","volume":"32","author":"LY Chuang","year":"2008","unstructured":"Chuang, L.Y., Chang, H.W., Tu, C.J., Yang, C.H.: Improved binary PSO for feature selection using gene expression data. Comput. Biol. Chem. 32(1), 29\u201338 (2008). https:\/\/doi.org\/10.1016\/j.compbiolchem.2007.09.005","journal-title":"Comput. Biol. Chem."},{"key":"174_CR71","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.knosys.2015.03.009","volume":"83","author":"A Wang","year":"2015","unstructured":"Wang, A., An, N., Chen, G., Li, L., Alterovitz, G.: Accelerating wrapper-based feature selection with K-nearest-neighbor. Knowl.-Based Syst. 83, 81\u201391 (2015). https:\/\/doi.org\/10.1016\/j.knosys.2015.03.009","journal-title":"Knowl.-Based Syst."},{"key":"174_CR72","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.ins.2013.10.012","volume":"258","author":"SS Shreem","year":"2014","unstructured":"Shreem, S.S., Abdullah, S., Nazri, M.Z.A.: Hybridising harmony search with a Markov blanket for gene selection problems. Inf. Sci. 258, 108\u2013121 (2014). https:\/\/doi.org\/10.1016\/j.ins.2013.10.012","journal-title":"Inf. Sci."},{"issue":"11","key":"174_CR73","doi-asserted-by":"publisher","first-page":"3236","DOI":"10.1016\/j.patcog.2007.02.007","volume":"40","author":"Z Zhu","year":"2007","unstructured":"Zhu, Z., Ong, Y.S., Dash, M.: Markov blanket-embedded genetic algorithm for gene selection. Pattern Recogn. 40(11), 3236\u20133248 (2007). https:\/\/doi.org\/10.1016\/j.patcog.2007.02.007","journal-title":"Pattern Recogn."},{"key":"174_CR74","unstructured":"Chuang, L.Y., Ke, C.H., Yang, C.H.: A hybrid both filter and wrapper feature selection method for microarray classification. arXiv:1612.08669 (2016)"},{"issue":"3","key":"174_CR75","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.bbe.2016.05.001","volume":"36","author":"M Mollaee","year":"2016","unstructured":"Mollaee, M., Moattar, M.H.: A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification. Biocybernet. Biomed. Eng. 36(3), 521\u2013529 (2016). https:\/\/doi.org\/10.1016\/j.bbe.2016.05.001","journal-title":"Biocybernet. Biomed. Eng."},{"key":"174_CR76","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1016\/j.asoc.2015.10.037","volume":"38","author":"J Apolloni","year":"2016","unstructured":"Apolloni, J., Leguizam\u00f3n, G., Alba, E.: Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl. Soft Comput. 38, 922\u2013932 (2016). https:\/\/doi.org\/10.1016\/j.asoc.2015.10.037","journal-title":"Appl. Soft Comput."},{"issue":"8","key":"174_CR77","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.1166\/jmihi.2017.2266","volume":"7","author":"A Sharma","year":"2017","unstructured":"Sharma, A., Rani, R.: An optimized framework for cancer classification using deep learning and genetic algorithm. J. Med. Imaging Health Inf. 7(8), 1851\u20131856 (2017). https:\/\/doi.org\/10.1166\/jmihi.2017.2266","journal-title":"J. Med. Imaging Health Inf."},{"key":"174_CR78","doi-asserted-by":"publisher","first-page":"106963","DOI":"10.1016\/j.compeleceng.2020.106963","volume":"90","author":"A Chaudhuri","year":"2021","unstructured":"Chaudhuri, A., Sahu, T.P.: A hybrid feature selection method based on Binary Jaya algorithm for micro-array data classification. Comput. Electr. Eng. 90, 106963 (2021). https:\/\/doi.org\/10.1016\/j.compeleceng.2020.106963","journal-title":"Comput. Electr. Eng."},{"key":"174_CR79","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1080\/00031305.1981.10479327","volume":"1981","author":"WJ Conover","year":"1981","unstructured":"Conover, W.J., Iman, R.L.: Rank transformations as a bridge between parametric and nonparametric statistics. Am. Stat. 1981, 124\u2013129 (1981). https:\/\/doi.org\/10.1080\/00031305.1981.10479327","journal-title":"Am. Stat."}],"container-title":["Iran Journal of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-024-00174-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42044-024-00174-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-024-00174-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T16:08:46Z","timestamp":1719331726000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42044-024-00174-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,8]]},"references-count":79,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["174"],"URL":"https:\/\/doi.org\/10.1007\/s42044-024-00174-z","relation":{},"ISSN":["2520-8438","2520-8446"],"issn-type":[{"value":"2520-8438","type":"print"},{"value":"2520-8446","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,8]]},"assertion":[{"value":"30 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}