{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T22:31:18Z","timestamp":1774650678739,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T00:00:00Z","timestamp":1551830400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Korea governmen","award":["2016-0-00133"],"award-info":[{"award-number":["2016-0-00133"]}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2016R1A6A3A11931385"],"award-info":[{"award-number":["2016R1A6A3A11931385"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2017R1A2B2009095"],"award-info":[{"award-number":["2017R1A2B2009095"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"name":"second Brain Korea 21 PLUS project, and Samsung Electronics"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s11227-019-02795-9","type":"journal-article","created":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T10:38:24Z","timestamp":1551868704000},"page":"5261-5279","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["CBCH (clustering-based convex hull) for reducing training time of support vector machine"],"prefix":"10.1007","volume":"75","author":[{"given":"Pardis","family":"Birzhandi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2833-7628","authenticated-orcid":false,"given":"Hee Yong","family":"Youn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,6]]},"reference":[{"key":"2795_CR1","first-page":"2632","volume":"8","author":"Y Yao","year":"2013","unstructured":"Yao Y, Liu Y, Yu Y, Xu H, Lv W, Li Z, Chen X (2013) K-SVM: an effective SVM algorithm based on K-means clustering. J Comput 8:2632\u20132639","journal-title":"J Comput"},{"key":"2795_CR2","doi-asserted-by":"crossref","unstructured":"Varadwaj P, Purohit N, Arora B (2009) Detection of splice sites using support vector machine. In: International Conference on Contemporary Computing, pp 493\u2013502","DOI":"10.1007\/978-3-642-03547-0_47"},{"key":"2795_CR3","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1016\/j.patrec.2010.02.015","volume":"31","author":"MA Kumar","year":"2010","unstructured":"Kumar MA, Gopal M (2010) A comparison study on multiple binary-class SVM methods for unilabel text categorization. Pattern Recogn Lett 31:1437\u20131444","journal-title":"Pattern Recogn Lett"},{"key":"2795_CR4","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1016\/j.asoc.2006.04.002","volume":"7","author":"V Mitra","year":"2007","unstructured":"Mitra V, Wang C-J, Banerjee S (2007) Text classification: a least square support vector machine approach. Appl Soft Comput 7:908\u2013914","journal-title":"Appl Soft Comput"},{"key":"2795_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0925-2312(03)00373-4","volume":"55","author":"AVD S\u00e1nchez","year":"2003","unstructured":"S\u00e1nchez AVD (2003) Advanced support vector machines and kernel methods. Neurocomputing 55:5\u201320","journal-title":"Neurocomputing"},{"key":"2795_CR6","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1016\/j.patrec.2005.03.006","volume":"26","author":"J Dong","year":"2005","unstructured":"Dong J, Krzy\u017cak A, Suen CY (2005) An improved handwritten Chinese character recognition system using support vector machine. Pattern Recogn Lett 26:1849\u20131856","journal-title":"Pattern Recogn Lett"},{"key":"2795_CR7","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/j.measurement.2006.10.010","volume":"40","author":"Y Yang","year":"2007","unstructured":"Yang Y, Yu D, Cheng J (2007) A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement 40:943\u2013950","journal-title":"Measurement"},{"key":"2795_CR8","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1016\/j.ymssp.2007.02.003","volume":"21","author":"S Abbasion","year":"2007","unstructured":"Abbasion S, Rafsanjani A, Farshidianfar A, Irani N (2007) Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech Syst Signal Process 21:2933\u20132945","journal-title":"Mech Syst Signal Process"},{"key":"2795_CR9","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.neucom.2014.07.038","volume":"149","author":"M Zeng","year":"2015","unstructured":"Zeng M, Yang Y, Zheng J, Cheng J (2015) Maximum margin classification based on flexible convex hulls. Neurocomputing 149:957\u2013965","journal-title":"Neurocomputing"},{"key":"2795_CR10","unstructured":"Bennett KP, Bredensteiner EJ (2000) Duality and geometry in SVM classifiers. In: ICML, pp 57\u201364"},{"key":"2795_CR11","volume-title":"Estimation of dependences based on empirical data","author":"VN Vapnik","year":"1982","unstructured":"Vapnik VN, Kotz S (1982) Estimation of dependences based on empirical data. Springer, New York"},{"key":"2795_CR12","unstructured":"Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical report MSR-TR-98-14, Microsoft research"},{"key":"2795_CR13","doi-asserted-by":"crossref","unstructured":"Awad M, Khan L, Bastani F, Yen I-L (2004) An effective support vector machines (SVMs) performance using hierarchical clustering. In: 16th IEEE International Conference on Tools with Artificial Intelligence, pp 663\u2013667","DOI":"10.1109\/ICTAI.2004.26"},{"key":"2795_CR14","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s10618-005-0005-7","volume":"11","author":"H Yu","year":"2005","unstructured":"Yu H, Yang J, Han J, Li X (2005) Making SVMs scalable to large data sets using hierarchical cluster indexing. Data Min Knowl Discov 11:295\u2013321","journal-title":"Data Min Knowl Discov"},{"key":"2795_CR15","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1016\/S0031-3203(03)00062-1","volume":"36","author":"B Heisele","year":"2003","unstructured":"Heisele B, Serre T, Prentice S, Poggio T (2003) Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recogn 36:2007\u20132017","journal-title":"Pattern Recogn"},{"key":"2795_CR16","doi-asserted-by":"crossref","unstructured":"Sohn S, Dagli CH (2001) Advantages of using fuzzy class memberships in self-organizing map and support vector machines. In: Proceedings 2001. IJCNN\u201901. International Joint Conference on Neural Networks, pp 1886\u20131890","DOI":"10.1109\/IJCNN.2001.938451"},{"key":"2795_CR17","doi-asserted-by":"crossref","unstructured":"Cervantes J, Li X, Yu W (2006) Support vector machine classification based on fuzzy clustering for large data sets. In: Mexican International Conference on Artificial Intelligence, pp 572\u2013582","DOI":"10.1007\/11925231_54"},{"key":"2795_CR18","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3906\/elk-1304-139","volume":"24","author":"ON Almasi","year":"2016","unstructured":"Almasi ON, Rouhani M (2016) Fast and de-noise support vector machine training method based on fuzzy clustering method for large real world datasets. Turk J Electr Eng Comput Sci 24:219\u2013233","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"2795_CR19","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1016\/j.neucom.2007.07.028","volume":"71","author":"J Cervantes","year":"2008","unstructured":"Cervantes J, Li X, Yu W, Li K (2008) Support vector machine classification for large data sets via minimum enclosing ball clustering. Neurocomputing 71:611\u2013619","journal-title":"Neurocomputing"},{"key":"2795_CR20","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2014.10.102","volume":"172","author":"X-J Shen","year":"2016","unstructured":"Shen X-J, Mu L, Li Z, Wu H-X, Gou J-P, Chen X (2016) Large-scale support vector machine classification with redundant data reduction. Neurocomputing 172:189\u2013197","journal-title":"Neurocomputing"},{"key":"2795_CR21","doi-asserted-by":"crossref","unstructured":"Shen X, Li Z, Jiang Z, Zhan Y (2013) Distributed SVM classification with redundant Data removing. Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings\/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing. IEEE, pp 866\u2013870","DOI":"10.1109\/GreenCom-iThings-CPSCom.2013.152"},{"key":"2795_CR22","first-page":"57","volume":"2","author":"R Koggalage","year":"2004","unstructured":"Koggalage R, Halgamuge S (2004) Reducing the number of training samples for fast support vector machine classification. Neural Inf Process Lett Rev 2:57\u201365","journal-title":"Neural Inf Process Lett Rev"},{"key":"2795_CR23","doi-asserted-by":"crossref","unstructured":"De Almeida MB, de P\u00e1dua Braga A, Braga JP (2000) SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. In: Proceedings-2000. Sixth Brazilian Symposium on Neural Networks, pp 162\u2013167","DOI":"10.1109\/SBRN.2000.889732"},{"key":"2795_CR24","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1080\/03610918.2012.762388","volume":"43","author":"S Bang","year":"2014","unstructured":"Bang S, Jhun M (2014) Weighted support vector machine using k-means clustering. Commun Stat Simul Comput 43:2307\u20132324","journal-title":"Commun Stat Simul Comput"},{"key":"2795_CR25","doi-asserted-by":"publisher","first-page":"10348","DOI":"10.1016\/j.ijleo.2016.08.027","volume":"127","author":"W Xu","year":"2016","unstructured":"Xu W, Dong L (2016) A novel relative density based support vector machine. Opt Int J Light Electron Opt 127:10348\u201310354","journal-title":"Opt Int J Light Electron Opt"},{"key":"2795_CR26","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10489-009-0176-9","volume":"34","author":"C Li","year":"2011","unstructured":"Li C, Liu K, Wang H (2011) The incremental learning algorithm with support vector machine based on hyperplane-distance. Appl Intell 34:19\u201327","journal-title":"Appl Intell"},{"key":"2795_CR27","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1016\/j.ijleo.2015.06.010","volume":"126","author":"S Xia","year":"2015","unstructured":"Xia S, Xiong Z, Luo Y, Dong L (2015) A method to improve support vector machine based on distance to hyperplane. Opt Int J Light Electron Opt 126:2405\u20132410","journal-title":"Opt Int J Light Electron Opt"},{"key":"2795_CR28","doi-asserted-by":"publisher","first-page":"8526","DOI":"10.1109\/ACCESS.2017.2699662","volume":"5","author":"Z Sun","year":"2017","unstructured":"Sun Z, Guo Z, Liu C, Wang X, Liu J, Liu S (2017) Fast extended one-versus-rest multi-label support vector machine using approximate extreme points. IEEE Access 5:8526\u20138535","journal-title":"IEEE Access"},{"key":"2795_CR29","unstructured":"Crisp DJ, Burges CJ (2000) A geometric interpretation of v-SVM classifiers. In: Advances in neural information processing systems, pp 244\u2013250"},{"key":"2795_CR30","doi-asserted-by":"crossref","unstructured":"Mavroforakis ME, Sdralis M, Theodoridis S (2006) A novel SVM geometric algorithm based on reduced convex hulls. In: 18th International Conference on pattern Recognition (ICPR\u201906), pp 564\u2013568","DOI":"10.1109\/ICPR.2006.143"},{"key":"2795_CR31","first-page":"411","volume-title":"Convex hull in feature space for support vector machines","author":"E Osuna","year":"2002","unstructured":"Osuna E, De Castro O (2002) Convex hull in feature space for support vector machines. Springer, New York, pp 411\u2013419"},{"key":"2795_CR32","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1109\/TNN.2006.873281","volume":"17","author":"ME Mavroforakis","year":"2006","unstructured":"Mavroforakis ME, Theodoridis S (2006) A geometric approach to support vector machine (SVM) classification. IEEE Trans Neural Netw 17:671\u2013682","journal-title":"IEEE Trans Neural Netw"},{"key":"2795_CR33","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s00500-012-0954-x","volume":"17","author":"AL Chau","year":"2013","unstructured":"Chau AL, Li X, Yu W (2013) Large data sets classification using convex\u2013concave hull and support vector machine. Soft Comput 17:793\u2013804","journal-title":"Soft Comput"},{"key":"2795_CR34","unstructured":"Chau AL, Li X, Yu W (2013) Convex-concave hull for classification with support vector machine. In: 2012 IEEE 12th International Conference on Data Mining Workshops, pp 431\u2013438"},{"key":"2795_CR35","unstructured":"Nalepa J, Kawulok M (2018) Selecting training sets for support vector machines: a review. Artificial Intelligence Review, pp 1\u201344"},{"key":"2795_CR36","doi-asserted-by":"crossref","unstructured":"Nalepa J, Kawulok M (2014) Adaptive genetic algorithm to select training data for support vector machines. In: European Conference on the Applications of Evolutionary Computation, pp 514\u2013525","DOI":"10.1007\/978-3-662-45523-4_42"},{"key":"2795_CR37","doi-asserted-by":"crossref","unstructured":"Kawulok M, Nalepa J (2012) Support vector machines training data selection using a genetic algorithm. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp 557\u2013565","DOI":"10.1007\/978-3-642-34166-3_61"},{"key":"2795_CR38","doi-asserted-by":"crossref","unstructured":"Nalepa J, Kawulok M (2014) A memetic algorithm to select training data for support vector machines. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp 573\u2013580","DOI":"10.1145\/2576768.2598370"},{"key":"2795_CR39","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.1007\/s00500-015-1642-4","volume":"20","author":"J Nalepa","year":"2016","unstructured":"Nalepa J, Kawulok M (2016) Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows. Soft Comput 20:2309\u20132327","journal-title":"Soft Comput"},{"key":"2795_CR40","doi-asserted-by":"crossref","unstructured":"Nalepa J, siminski K, Kawulok M (2015) Towards parameter-less support vector machines. In: ACPR, pp 211\u2013215","DOI":"10.1109\/ACPR.2015.7486496"},{"key":"2795_CR41","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1145\/235815.235821","volume":"22","author":"CB Barber","year":"1996","unstructured":"Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM TOMS 22:469\u2013483","journal-title":"ACM TOMS"},{"key":"2795_CR42","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/MSP.2007.361610","volume":"24","author":"S Theodoridis","year":"2007","unstructured":"Theodoridis S, Mavroforakis M (2007) Reduced convex hulls: a geometric approach to support vector machines [lecture notes]. IEEE Signal Process Mag 24:119\u2013122","journal-title":"IEEE Signal Process Mag"},{"key":"2795_CR43","first-page":"1083","volume":"9","author":"Y Li","year":"2012","unstructured":"Li Y, Wang Y, He G (2012) Clustering-based distributed support vector machine in wireless sensor networks. J Inf Comput Sci 9:1083\u20131096","journal-title":"J Inf Comput Sci"},{"key":"2795_CR44","unstructured":"De Berg M, Van Kreveld M, Overmars M, Schwarzkopf OC (2000) Computational geometry. In: Computational geometry. Springer, pp 1\u201317"},{"key":"2795_CR45","doi-asserted-by":"crossref","unstructured":"Li X, Cervantes J, Yu W (2010) A novel SVM classification method for large data sets. In: 2010 IEEE International Conference on Granular Computing, pp 297\u2013302","DOI":"10.1109\/GrC.2010.46"},{"key":"2795_CR46","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1504\/IJBIDM.2005.007318","volume":"1","author":"J Wang","year":"2005","unstructured":"Wang J, Wu X, Zhang C (2005) Support vector machines based on K-means clustering for real-time business intelligence systems. Int J Bus Intell Data Min 1:54\u201364","journal-title":"Int J Bus Intell Data Min"},{"key":"2795_CR47","doi-asserted-by":"crossref","unstructured":"Inaba M, Katoh N, Imai H (1994) Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. In: Proceedings of the tenth annual symposium on Computational geometry, pp 332\u2013339","DOI":"10.1145\/177424.178042"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-019-02795-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-019-02795-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-019-02795-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,5]],"date-time":"2020-03-05T00:09:35Z","timestamp":1583366975000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-019-02795-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,6]]},"references-count":47,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["2795"],"URL":"https:\/\/doi.org\/10.1007\/s11227-019-02795-9","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,6]]},"assertion":[{"value":"6 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}