{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:52:59Z","timestamp":1778665979226,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T00:00:00Z","timestamp":1527033600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"The China Scholarship Council Foundation","award":["201408410287"],"award-info":[{"award-number":["201408410287"]}]},{"name":"The High level Scientific research cultivation Foundation of Henan University of Science and Technology","award":["2015GJB010"],"award-info":[{"award-number":["2015GJB010"]}]},{"name":"The Region Nature Science Foundation of China","award":["61562001"],"award-info":[{"award-number":["61562001"]}]},{"name":"The National Social Science Foundation of China","award":["13BGL063"],"award-info":[{"award-number":["13BGL063"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s10489-018-1185-3","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T01:00:15Z","timestamp":1527037215000},"page":"4023-4046","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A rough \u03bd-twin support vector regression machine"],"prefix":"10.1007","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1952-9513","authenticated-orcid":false,"given":"Zhenxia","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuandong","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,5,23]]},"reference":[{"issue":"3","key":"1185_CR1","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"10","key":"1185_CR2","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1080\/03610918.2011.589744","volume":"40","author":"C Shen","year":"2011","unstructured":"Shen C, Wang X (2011) Analysis of convertible bond value based on integration of support vector machine and copula function. Commun Stat Simul Comput 40(10):1563\u20131575","journal-title":"Commun Stat Simul Comput"},{"key":"1185_CR3","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1016\/j.asoc.2014.02.002","volume":"19","author":"PC Deka","year":"2014","unstructured":"Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372\u2013386","journal-title":"Appl Soft Comput"},{"key":"1185_CR4","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.asoc.2015.05.058","volume":"35","author":"MS Flores","year":"2015","unstructured":"Flores MS, Verbraken T et al. (2015) Profit-based feature selection using support vector machines general framework and an application for customer retention. Appl Soft Comput 35:740\u2013748","journal-title":"Appl Soft Comput"},{"key":"1185_CR5","unstructured":"Sch\u00f6lkopf PB, Burgest C, Vapnik V (1995) Extracting support data for a given task. In: Proceedings of the first international conference on knowledge discovery and data mining. AAAI Press, Menlo Park, pp 252\u2013257"},{"issue":"3","key":"1185_CR6","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199\u2013222","journal-title":"Stat Comput"},{"key":"1185_CR7","first-page":"111","volume-title":"Support vector regression with automatic accuracy control, ICANN 98","author":"B Sch\u00f6lkopf","year":"1998","unstructured":"Sch\u00f6lkopf B, Bartlett P, Smola A et al. (1998) Support vector regression with automatic accuracy control, ICANN 98. Springer, London, pp 111\u2013116"},{"key":"1185_CR8","volume-title":"Statistical learning theory","author":"V Vapnik","year":"1998","unstructured":"Vapnik V, Vapnik V (1998) Statistical learning theory. Wiley, New York"},{"key":"1185_CR9","doi-asserted-by":"publisher","DOI":"10.1201\/b14297","volume-title":"Support vector machines: optimization based theory, algorithms, and extensions","author":"N Deng","year":"2012","unstructured":"Deng N, Tian Y, Zhang C (2012) Support vector machines: optimization based theory, algorithms, and extensions. CRC Press, Boca Raton"},{"key":"1185_CR10","doi-asserted-by":"crossref","unstructured":"Suykens JAK, Van Gestel T, De Brabanter J (2002) Least squares support vector machines. World Scientific","DOI":"10.1142\/5089"},{"issue":"1","key":"1185_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TPAMI.2006.17","volume":"28","author":"OL Mangasarian","year":"2006","unstructured":"Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69\u201374","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"1185_CR12","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"R Khemchandani","year":"2007","unstructured":"Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905\u2013910. CampsLJ","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"1185_CR13","doi-asserted-by":"publisher","first-page":"7535","DOI":"10.1016\/j.eswa.2008.09.066","volume":"36","author":"MA Kumar","year":"2009","unstructured":"Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535\u20137543","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1185_CR14","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1109\/TNN.2011.2130540","volume":"22","author":"YH Shao","year":"2011","unstructured":"Shao YH, Zhang C, Wang X, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962\u2013968","journal-title":"IEEE Trans Neural Netw"},{"issue":"10","key":"1185_CR15","doi-asserted-by":"publisher","first-page":"2678","DOI":"10.1016\/j.patcog.2011.03.031","volume":"44","author":"X Peng","year":"2011","unstructured":"Peng X (2011) TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn 44(10):2678\u20132692","journal-title":"Pattern Recogn"},{"issue":"3","key":"1185_CR16","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.neunet.2009.07.002","volume":"23","author":"X Peng","year":"2010","unstructured":"Peng X (2010) TSVR: An efficient twin support vector machine for regression. Neural Netw 23(3):365\u2013372","journal-title":"Neural Netw"},{"key":"1185_CR17","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.neunet.2015.10.007","volume":"74","author":"R Khemchandani","year":"2016","unstructured":"Khemchandani R, Goyal K, Chandra S (2016) TWSVR: Regression via twin support vector machine. Neural Netw 74:14\u201321","journal-title":"Neural Netw"},{"issue":"3","key":"1185_CR18","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s00521-010-0454-9","volume":"21","author":"X Chen","year":"2012","unstructured":"Chen X, Yang J, Liang J et al. (2012) Smooth twin support vector regression. Neural Comput Appl 21 (3):505\u2013513","journal-title":"Neural Comput Appl"},{"issue":"4","key":"1185_CR19","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1007\/s10489-015-0728-0","volume":"44","author":"M Tanveer","year":"2016","unstructured":"Tanveer M, Shubham K, Aldhaifallah M, Nisar KS (2016) An efficient implicit regularized Lagrangian twin support vector regression. Appl Intell 44(4):831\u2013848","journal-title":"Appl Intell"},{"issue":"1","key":"1185_CR20","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-012-0924-3","volume":"23","author":"YH Shao","year":"2013","unstructured":"Shao YH, Zhang C, Yang ZM, Jing L, Deng NY (2013) An \u03b5-twin support vector machine for regression. Neural Comput Appl 23(1):175\u2013185","journal-title":"Neural Comput Appl"},{"issue":"3","key":"1185_CR21","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1007\/s10489-016-0860-5","volume":"46","author":"R Rastogi","year":"2017","unstructured":"Rastogi R, Anand P, Chandra S (2017) A \u03bd-twin support vector machine based regression with automatic accuracy control. Appl Intell 46(3):670\u2013683","journal-title":"Appl Intell"},{"issue":"1","key":"1185_CR22","first-page":"41","volume":"177","author":"Z Pawlak","year":"2007","unstructured":"Pawlak Z, Skowron A (2007) Rough sets and Boolean reasoning. Information rough 177(1):41\u201373. Sciences 177(2007) 21\u201373","journal-title":"Information rough"},{"issue":"1","key":"1185_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0020-0255(02)00197-4","volume":"147","author":"Z Law Pawlak","year":"2002","unstructured":"Law Pawlak Z (2002) Rough sets and intelligent data analysis. Inf Sci 147(1):1\u201312","journal-title":"Inf Sci"},{"key":"1185_CR24","doi-asserted-by":"crossref","unstructured":"Chen RC, Cheng KF, Chen YH et al. (2009) Using rough set and support vector machine for network intrusion detection system. In: First Asian conference on intelligent information and database systems, ACIIDS 2009. IEEE, 465\u2013470","DOI":"10.1109\/ACIIDS.2009.59"},{"issue":"9","key":"1185_CR25","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1016\/j.ins.2007.12.012","volume":"178","author":"J Zhang","year":"2008","unstructured":"Zhang J, Wang Y (2008) A rough margin based support vector machine. Inf Sci 178(9):2204\u20132214","journal-title":"Inf Sci"},{"issue":"2","key":"1185_CR26","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.ejor.2009.10.023","volume":"206","author":"P Lingras","year":"2010","unstructured":"Lingras P, Butz CJ (2010) Rough support vector regression. Eur J Oper Res 206(2):445\u2013455","journal-title":"Eur J Oper Res"},{"issue":"6","key":"1185_CR27","doi-asserted-by":"publisher","first-page":"9793","DOI":"10.1016\/j.eswa.2009.02.007","volume":"36","author":"Y Zhao","year":"2009","unstructured":"Zhao Y, Sun J (2009) Rough \u03bd-support vector regression. Expert Syst Appl 36(6):9793\u20139798","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1185_CR28","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1007\/s00521-011-0565-y","volume":"21","author":"Y Xu","year":"2012","unstructured":"Xu Y, Wang L, Zhong P (2012) A rough margin-based \u03bd-twin support vector machine. Neural Comput Appl 21(6):1307\u20131317","journal-title":"Neural Comput Appl"},{"key":"1185_CR29","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.knosys.2017.05.004","volume":"128","author":"H Wang","year":"2017","unstructured":"Wang H, Zhou Z (2017) An improved rough margin-based \u03bd-twin bounded support vector machine. Knowl-Based Syst 128:125\u2013138","journal-title":"Knowl-Based Syst"},{"key":"1185_CR30","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.knosys.2014.08.008","volume":"71","author":"Y Xu","year":"2014","unstructured":"Xu Y, Yu J, Zhang Y (2014) KNN-Based weighted rough \u03bd-twin support vector machine. Knowl-Based Syst 71:303\u2013313","journal-title":"Knowl-Based Syst"},{"key":"1185_CR31","unstructured":"Karush W (2014) Minima of functions of several variables with inequalities as side conditions, Traces and Emergence of Nonlinear Programming. Springer Basel, pp 217\u2013245"},{"key":"1185_CR32","unstructured":"Blake CL UCI repository of machine learning databases. \n                    http:\/\/archive.ics.uci.edu\/ml\/index.php"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-018-1185-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1185-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1185-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T01:36:55Z","timestamp":1558575415000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-018-1185-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,23]]},"references-count":32,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["1185"],"URL":"https:\/\/doi.org\/10.1007\/s10489-018-1185-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,23]]},"assertion":[{"value":"23 May 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}