{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:36:23Z","timestamp":1772303783329,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"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":["J Supercomput"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11227-023-05385-y","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T12:02:36Z","timestamp":1684497756000},"page":"18777-18799","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A lightweight knowledge-based PSO for SVM hyper-parameters tuning in a dynamic environment"],"prefix":"10.1007","volume":"79","author":[{"given":"Dhruba Jyoti","family":"Kalita","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vibhav Prakash","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinay","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"5385_CR1","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","volume":"415","author":"L Yang","year":"2020","unstructured":"Yang L, Shami A (2020) On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415:295\u2013316","journal-title":"Neurocomputing"},{"key":"5385_CR2","doi-asserted-by":"crossref","unstructured":"Kalita DJ, Singh VP, Kumar V (2020) A survey on SVM hyper-parameters optimization techniques. In: Social networking and computational intelligence: proceedings of SCI-2018. Springer Singapore. (pp. 243-256)","DOI":"10.1007\/978-981-15-2071-6_20"},{"key":"5385_CR3","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, (Vol. 4, pp. 1942\u20131948)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"5385_CR4","unstructured":"Vapnik V, Vapnik V (1998) Statistical learning theory (pp. 156\u2013160)"},{"key":"5385_CR5","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","volume":"408","author":"J Cervantes","year":"2020","unstructured":"Cervantes J, Garcia-Lamont F, Rodr\u00edguez-Mazahua L, Lopez A (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408:189\u2013215","journal-title":"Neurocomputing"},{"key":"5385_CR6","doi-asserted-by":"crossref","unstructured":"Feurer M, Hutter F (2019) Hyperparameter optimization. In: Automated machine learning. Springer, Cham. (pp. 3\u201333)","DOI":"10.1007\/978-3-030-05318-5_1"},{"issue":"2","key":"5385_CR7","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1007\/s00500-019-03957-w","volume":"24","author":"DJ Kalita","year":"2020","unstructured":"Kalita DJ, Singh S (2020) SVM hyper-parameters optimization using quantized multi-PSO in dynamic environment. Soft Comput 24(2):1225\u20131241","journal-title":"Soft Comput"},{"key":"5385_CR8","doi-asserted-by":"publisher","first-page":"114139","DOI":"10.1016\/j.eswa.2020.114139","volume":"168","author":"DJ Kalita","year":"2021","unstructured":"Kalita DJ, Singh VP, Kumar V (2021) A dynamic framework for tuning SVM hyper parameters based on moth-flame optimization and knowledge-based-search. Expert Syst Appl 168:114139","journal-title":"Expert Syst Appl"},{"key":"5385_CR9","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/978-0-387-39940-9_565","volume":"5","author":"P Refaeilzadeh","year":"2009","unstructured":"Refaeilzadeh P, Tang L, Liu H (2009) Cross-validation. Encyclop Database Syst 5:532\u2013538","journal-title":"Encyclop Database Syst"},{"issue":"1\u20133","key":"5385_CR10","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1023\/A:1012450327387","volume":"46","author":"O Chapelle","year":"2002","unstructured":"Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1\u20133):131\u2013159","journal-title":"Mach Learn"},{"issue":"10","key":"5385_CR11","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1016\/j.patcog.2005.03.011","volume":"38","author":"N Ayat","year":"2005","unstructured":"Ayat N, Cheriet M, Suen C (2005) Automatic model selection for the optimization of SVM kernels. Patt Recogn 38(10):1733\u20131745","journal-title":"Patt Recogn"},{"key":"5385_CR12","doi-asserted-by":"crossref","unstructured":"Alibrahim H, Ludwig SA (2021) Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization. In: 2021 IEEE congress on evolutionary computation (CEC), IEEE, pp. 1551\u20131559","DOI":"10.1109\/CEC45853.2021.9504761"},{"issue":"1","key":"5385_CR13","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.csda.2007.02.013","volume":"52","author":"CM Huang","year":"2007","unstructured":"Huang CM, Lee YJ, Lin DK, Huang SY (2007) Model selection for support vector machines via uniform design. Comput Stat Data Anal 52(1):335\u2013346","journal-title":"Comput Stat Data Anal"},{"key":"5385_CR14","unstructured":"Huang Q, Mao J, Liu Y (2012) An improved grid search algorithm of SVR parameters optimization. In: 2012 IEEE 14th International Conference on Communication Technology, IEEE, pp. 1022\u20131026"},{"key":"5385_CR15","doi-asserted-by":"crossref","unstructured":"Chunhong Z, Licheng J (2004) Automatic parameters selection for SVM based on GA. In: Proceedings of the 5th world congress on intelligent control and automation, pp. 1869\u20131872","DOI":"10.1109\/WCICA.2004.1341000"},{"key":"5385_CR16","doi-asserted-by":"crossref","unstructured":"Cohen G, Hilario M, Geissbuhler A (2004) Model selection for support vector classifiers via genetic algorithms. An application to medical decision support. In: Proceedings of the 5th international symposium on biological and medical data analysis, pp.200\u2013211","DOI":"10.1007\/978-3-540-30547-7_21"},{"key":"5385_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-33019-4_9","author":"T Suttorp","year":"2006","unstructured":"Suttorp T, Igel C (2006) Multi-objective optimization of support vector machines. Multi-Object Mach Learn. https:\/\/doi.org\/10.1007\/3-540-33019-4_9","journal-title":"Multi-Object Mach Learn"},{"key":"5385_CR18","unstructured":"Friedrichs F, Igel C (2004) Evolutionary tuning of multiple SVM parameters, In: Proceedings of the 12th european symposium on artificial neural networks, pp- 519\u2013524"},{"issue":"4","key":"5385_CR19","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1016\/j.asoc.2007.10.012","volume":"8","author":"SW Lin","year":"2008","unstructured":"Lin SW, Lee ZJ, Chen SC, Tseng TY (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8(4):1505\u20131512","journal-title":"Appl Soft Comput"},{"key":"5385_CR20","doi-asserted-by":"publisher","first-page":"7164","DOI":"10.1109\/ACCESS.2017.2779794","volume":"6","author":"A Rojas-Dom\u00ednguez","year":"2017","unstructured":"Rojas-Dom\u00ednguez A, Padierna LC, Valadez JMC, Puga-Soberanes HJ, Fraire HJ (2017) Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access 6:7164\u20137176","journal-title":"IEEE Access"},{"key":"5385_CR21","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/548483","author":"X Liu","year":"2014","unstructured":"Liu X, Fu H (2014) PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses. Sci World J. https:\/\/doi.org\/10.1155\/2014\/548483","journal-title":"Sci World J"},{"issue":"9","key":"5385_CR22","doi-asserted-by":"publisher","first-page":"6618","DOI":"10.1016\/j.eswa.2010.03.067","volume":"37","author":"X Zhang","year":"2010","unstructured":"Zhang X, Chen X, He Z (2010) An ACO-based algorithm for parameter optimization of support vector machines. Expert Syst Appl 37(9):6618\u20136628","journal-title":"Expert Syst Appl"},{"issue":"2","key":"5385_CR23","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s10489-010-0260-1","volume":"36","author":"L Dio\u015fan","year":"2012","unstructured":"Dio\u015fan L, Rogozan A, Pecuchet JP (2012) Improving classification performance of support vector machine by genetically optimising kernel shape and hyper-parameters. Appl Intell 36(2):280\u2013294","journal-title":"Appl Intell"},{"key":"5385_CR24","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.cor.2018.01.013","volume":"106","author":"A Candelieri","year":"2019","unstructured":"Candelieri A, Giordani I, Archetti F, Barkalov K, Meyerov I, Polovinkin A, Zolotykh N (2019) Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization. Comput Op Res 106:202\u2013209","journal-title":"Comput Op Res"},{"issue":"2","key":"5385_CR25","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/j.eswa.2005.12.008","volume":"32","author":"CH Wu","year":"2007","unstructured":"Wu CH, Tzeng GH, Goo YJ, Fang WC (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32(2):397\u2013408","journal-title":"Expert Syst Appl"},{"issue":"2","key":"5385_CR26","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s10489-016-0843-6","volume":"46","author":"AV Phan","year":"2017","unstructured":"Phan AV, Le Nguyen M, Bui LT (2017) Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Appl Intell 46(2):455\u2013469","journal-title":"Appl Intell"},{"issue":"3","key":"5385_CR27","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1007\/s10489-017-0994-0","volume":"48","author":"A Tharwat","year":"2018","unstructured":"Tharwat A, Hassanien AE (2018) Chaotic antlion algorithm for parameter optimization of support vector machine. Appl Intell 48(3):670\u2013686","journal-title":"Appl Intell"},{"key":"5385_CR28","unstructured":"Rai P, Daum\u00b4 e H, Venkatasubramanian S (2009) Streamed learning: One-pass SVMs. In: Twenty-First International Joint Conference on Artificial Intelligence"},{"issue":"8","key":"5385_CR29","doi-asserted-by":"publisher","first-page":"2550","DOI":"10.1016\/j.asoc.2012.04.001","volume":"12","author":"MN Kapp","year":"2012","unstructured":"Kapp MN, Sabourin R, Maupin P (2012) A dynamic model selection strategy for support vector machine classifiers. Appl Soft Comput 12(8):2550\u20132565","journal-title":"Appl Soft Comput"},{"key":"5385_CR30","volume-title":"Instrumentation, measurement, circuits and systems","author":"J Li","year":"2012","unstructured":"Li J, Chen X (2012) Online learning algorithm of direct support vector machine for regression based on matrix operation. In: Zhang T (ed) Instrumentation, measurement, circuits and systems. Springer, Berlin"},{"key":"5385_CR31","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.procs.2019.12.125","volume":"163","author":"NA Hitam","year":"2019","unstructured":"Hitam NA, Ismail AR, Saeed F (2019) An optimized support vector machine (SVM) based on particle swarm optimization (PSO) for cryptocurrency forecasting. Procedia Comput Sci 163:427\u2013433","journal-title":"Procedia Comput Sci"},{"key":"5385_CR32","doi-asserted-by":"crossref","unstructured":"Kalita DJ, Singh VP, Kumar V (2020) SVM hyper-parameters optimization using multi-PSO for intrusion detection. In: Social networking and computational intelligence: proceedings of SCI-2018. Springer Singapore, pp. 227\u2013241","DOI":"10.1007\/978-981-15-2071-6_19"},{"key":"5385_CR33","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1007\/s00521-013-1341-y","volume":"24","author":"C Sudheer","year":"2014","unstructured":"Sudheer C, Maheswaran R, Panigrahi BK, Mathur S (2014) A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput Appl 24:1381\u20131389","journal-title":"Neural Comput Appl"},{"issue":"6","key":"5385_CR34","doi-asserted-by":"publisher","first-page":"453","DOI":"10.3233\/IDA-1999-3604","volume":"3","author":"M Black","year":"1999","unstructured":"Black M, Hickey RJ (1999) Maintaining the performance of a learned classifier under concept drift. Intell Data Anal 3(6):453\u2013474","journal-title":"Intell Data Anal"},{"issue":"2","key":"5385_CR35","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3233\/IDA-2002-6203","volume":"6","author":"M Last","year":"2002","unstructured":"Last M (2002) Online classification of nonstationary data streams. Intell Data Anal 6(2):129\u2013147","journal-title":"Intell Data Anal"},{"issue":"3","key":"5385_CR36","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.inffus.2005.05.005","volume":"9","author":"L Cohen","year":"2008","unstructured":"Cohen L, Avrahami-Bakish G, Last M, Kandel A, Kipersztok O (2008) Real-time data mining of non-stationary data streams from sensor networks. Inform Fusion 9(3):344\u2013353","journal-title":"Inform Fusion"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05385-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05385-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05385-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:27:10Z","timestamp":1729452430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05385-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,19]]},"references-count":36,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["5385"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05385-y","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,19]]},"assertion":[{"value":"5 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2023","order":2,"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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals by any authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}