{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:29:53Z","timestamp":1768091393911,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s10489-021-02402-6","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T15:02:51Z","timestamp":1623942171000},"page":"2634-2654","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Universum parametric-margin \u03bd-support vector machine for classification using the difference of convex functions algorithm"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0640-2161","authenticated-orcid":false,"given":"Hossein","family":"Moosaei","sequence":"first","affiliation":[]},{"given":"Fatemeh","family":"Bazikar","sequence":"additional","affiliation":[]},{"given":"Saeed","family":"Ketabchi","sequence":"additional","affiliation":[]},{"given":"Milan","family":"Hlad\u00edk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"2402_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.cmpb.2017.01.004","volume":"141","author":"Z Arabasadi","year":"2017","unstructured":"Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods Prog Biomed 141:19\u201326","journal-title":"Comput Methods Prog Biomed"},{"issue":"1","key":"2402_CR2","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1137\/18M123339X","volume":"30","author":"FJ Arag\u00f3nArtacho","year":"2020","unstructured":"Arag\u00f3nArtacho FJ, Vuong PT (2020) The boosted difference of convex functions algorithm for nonsmooth functions. SIAM J Optim 30(1):980\u20131006","journal-title":"SIAM J Optim"},{"issue":"2","key":"2402_CR3","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s10013-020-00400-8","volume":"48","author":"FA Artacho","year":"2020","unstructured":"Artacho FA, Campoy R, Vuong PT (2020) Using positive spanning sets to achieve d-stationarity with the boosted dc algorithm. Vietnam J Math 48(2):363","journal-title":"Vietnam J Math"},{"issue":"1","key":"2402_CR4","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10107-017-1180-1","volume":"169","author":"FJA Artacho","year":"2018","unstructured":"Artacho FJA, Fleming RM, Vuong PT (2018) Accelerating the DC algorithm for smooth functions. Math Program 169(1):95\u2013118","journal-title":"Math Program"},{"key":"2402_CR5","doi-asserted-by":"crossref","unstructured":"Bazikar F, Ketabchi S, Moosaei H (2020) DC programming and DCA for parametric-margin \u03bd-support vector machine. Appl Intell:1\u201312","DOI":"10.1007\/s10489-019-01618-x"},{"key":"2402_CR6","first-page":"1369","volume":"20","author":"O Chapelle","year":"2007","unstructured":"Chapelle O, Agarwal A, Sinz F, Sch\u00f6lkopf B (2007) An analysis of inference with the universum. Adv Neural Inf Process Syst 20:1369\u20131376","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"2402_CR7","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1007\/s00521-011-0623-5","volume":"21","author":"X Chen","year":"2012","unstructured":"Chen X, Yang J, Liang J (2012) A flexible support vector machine for regression. Neural Comput Appl 21(8):2005\u20132013","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2402_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s13040-017-0155-3","volume":"10","author":"D Chicco","year":"2017","unstructured":"Chicco D (2017) Ten quick tips for machine learning in computational biology. BioData Min 10 (1):35","journal-title":"BioData Min"},{"key":"2402_CR9","doi-asserted-by":"crossref","unstructured":"Clarke FH (1990) Optimization and nonsmooth analysis. SIAM","DOI":"10.1137\/1.9781611971309"},{"key":"2402_CR10","unstructured":"Daniel WW (1990) Friedman two-way analysis of variance by ranks. Appl Nonparametr Stat:262\u2013274"},{"key":"2402_CR11","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.neucom.2016.11.026","volume":"225","author":"S Ding","year":"2017","unstructured":"Ding S, An Y, Zhang X, Wu F, Xue Y (2017) Wavelet twin support vector machines based on glowworm swarm optimization. Neurocomputing 225:157\u2013163","journal-title":"Neurocomputing"},{"issue":"2","key":"2402_CR12","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1049\/iet-ipr.2018.5977","volume":"14","author":"S Ding","year":"2019","unstructured":"Ding S, Shi S, Jia W (2019) Research on fingerprint classification based on twin support vector machine. IET Image Process 14(2):231\u2013235","journal-title":"IET Image Process"},{"issue":"11","key":"2402_CR13","doi-asserted-by":"publisher","first-page":"3119","DOI":"10.1007\/s00521-016-2245-4","volume":"28","author":"S Ding","year":"2017","unstructured":"Ding S, Zhang N, Zhang X, Wu F (2017) Twin support vector machine: theory, algorithm and applications. Neural Comput Appl 28(11):3119\u20133130","journal-title":"Neural Comput Appl"},{"key":"2402_CR14","unstructured":"Dua D, Graff C UCI machine learning repository (2019). https:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"2402_CR15","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"issue":"1","key":"2402_CR16","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.neunet.2009.08.001","volume":"23","author":"PY Hao","year":"2010","unstructured":"Hao PY (2010) New support vector algorithms with parametric insensitive\/margin model. Neural Netw 23(1):60\u201373","journal-title":"Neural Netw"},{"issue":"1","key":"2402_CR17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/BF01442169","volume":"11","author":"JB Hiriart-Urruty","year":"1984","unstructured":"Hiriart-Urruty JB, Strodiot JJ, Nguyen VH (1984) Generalized Hessian matrix and second-order optimality conditions for problems with c1,1 data. Appl Math Optim 11(1):43\u201356","journal-title":"Appl Math Optim"},{"key":"2402_CR18","unstructured":"Hsu CW, Chang CC, Lin C et al (2003) A practical guide to support vector classification"},{"issue":"6","key":"2402_CR19","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1080\/03610928008827904","volume":"9","author":"RL Iman","year":"1980","unstructured":"Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Commun Stat-Theory Methods 9(6):571\u2013595","journal-title":"Commun Stat-Theory Methods"},{"issue":"5","key":"2402_CR20","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"KR Jayadeva","year":"2007","unstructured":"Jayadeva KR, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"2402_CR21","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1007\/s10957-012-0044-3","volume":"154","author":"S Ketabchi","year":"2012","unstructured":"Ketabchi S, Moosaei H (2012) Minimum norm solution to the absolute value equation in the convex case. J Optim Theory Appl 154(3):1080\u20131087","journal-title":"J Optim Theory Appl"},{"issue":"1-2","key":"2402_CR22","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10479-017-2724-8","volume":"276","author":"S Ketabchi","year":"2019","unstructured":"Ketabchi S, Moosaei H, Razzaghi M, Pardalos PM (2019) An improvement on parametric \u03bd-support vector algorithm for classification. Ann Oper Res 276(1-2):155\u2013168","journal-title":"Ann Oper Res"},{"key":"2402_CR23","unstructured":"LeCun Y, Boser BE, Denker JS, Henderson D, Howard RE, Hubbard WE, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, pp 396\u2013404"},{"key":"2402_CR24","doi-asserted-by":"crossref","unstructured":"Lee YJ, Mangasarian OL (2001) RSVM: Reduced support vector machines. In: Proceedings of the 2001 SIAM International Conference on Data Mining. SIAM, pp 1\u201317","DOI":"10.1137\/1.9781611972719.13"},{"issue":"1","key":"2402_CR25","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s10586-017-0806-7","volume":"21","author":"M Li","year":"2018","unstructured":"Li M, Yu X, Ryu KH, Lee S, Theera-Umpon N (2018) Face recognition technology development with gabor, pca and svm methodology under illumination normalization condition. Clust Comput 21 (1):1117\u20131126","journal-title":"Clust Comput"},{"key":"2402_CR26","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.neucom.2018.06.040","volume":"313","author":"MD de Lima","year":"2018","unstructured":"de Lima MD, Costa NL, Barbosa R (2018) Improvements on least squares twin multi-class classification support vector machine. Neurocomputing 313:196\u2013205","journal-title":"Neurocomputing"},{"issue":"1","key":"2402_CR27","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TPAMI.2006.17","volume":"28","author":"OL Mangasarian","year":"2005","unstructured":"Mangasarian OL, Wild EW (2005) 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"},{"key":"2402_CR28","unstructured":"Moosaei H, Musicant D, Khosravi S, Hlad\u00edk M (2020) MC-NDC: multi-class normally distributed clustered datasets. Carleton College, University of Bojnord. https:\/\/github.com\/dmusican\/ndc"},{"key":"2402_CR29","unstructured":"Musicant D (1998) NDC: normally distributed clustered datasets"},{"key":"2402_CR30","first-page":"92","volume":"71","author":"WS Noble","year":"2004","unstructured":"Noble WS, et al. (2004) Support vector machine applications in computational biology. Kernel Methods Comput Biol 71:92","journal-title":"Kernel Methods Comput Biol"},{"issue":"3","key":"2402_CR31","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1080\/02331934.2011.649480","volume":"63","author":"PM Pardalos","year":"2014","unstructured":"Pardalos PM, Ketabchi S, Moosaei H (2014) Minimum norm solution to the positive semidefinite linear complementarity problem. Optimization 63(3):359\u2013369","journal-title":"Optimization"},{"key":"2402_CR32","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neunet.2012.09.004","volume":"36","author":"Z Qi","year":"2012","unstructured":"Qi Z, Tian Y, Shi Y (2012) Twin support vector machine with universum data. Neural Netw 36:112\u2013119","journal-title":"Neural Netw"},{"key":"2402_CR33","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Smola AJ, Bach F, et al. (2002) Learning with kernels: Support vector machines, Regularization, Optimization, and Beyond. MIT Press","DOI":"10.7551\/mitpress\/4175.001.0001"},{"issue":"5","key":"2402_CR34","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Sch\u00f6lkopf","year":"2000","unstructured":"Sch\u00f6lkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207\u20131245","journal-title":"Neural Comput"},{"issue":"1","key":"2402_CR35","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/s10489-015-0751-1","volume":"45","author":"M Tanveer","year":"2016","unstructured":"Tanveer M, Khan MA, Ho SS (2016) Robust energy-based least squares twin support vector machines. Appl Intell 45(1):174\u2013186","journal-title":"Appl Intell"},{"issue":"1s","key":"2402_CR36","first-page":"1","volume":"16","author":"M Tanveer","year":"2020","unstructured":"Tanveer M, Richhariya B, Khan R, Rashid A, Khanna P, Prasad M, Lin C (2020) Machine learning techniques for the diagnosis of alzheimer\u2019s disease: A review. ACM Trans Multimed Comput Commun Appl (TOMM) 16(1s):1\u201335","journal-title":"ACM Trans Multimed Comput Commun Appl (TOMM)"},{"issue":"3","key":"2402_CR37","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/0167-6377(96)00022-3","volume":"19","author":"PD Tao","year":"1996","unstructured":"Tao PD, et al. (1996) Numerical solution for optimization over the efficient set by dc optimization algorithms. Oper Res Lett 19(3):117\u2013128","journal-title":"Oper Res Lett"},{"issue":"2","key":"2402_CR38","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s40745-014-0018-4","volume":"1","author":"Y Tian","year":"2014","unstructured":"Tian Y, Qi Z (2014) Review on: twin support vector machines. Ann Data Sci 1(2):253\u2013277","journal-title":"Ann Data Sci"},{"key":"2402_CR39","volume-title":"Theory of pattern recognition","author":"V Vapnik","year":"1974","unstructured":"Vapnik V, Chervonenkis A (1974) Theory of pattern recognition. Moscow, Nauka"},{"issue":"4","key":"2402_CR40","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1007\/s10489-017-0984-2","volume":"48","author":"H Wang","year":"2018","unstructured":"Wang H, Zhou Z, Xu Y (2018) An improved \u03bd-twin bounded support vector machine. Appl Intell 48(4):1041\u20131053","journal-title":"Appl Intell"},{"key":"2402_CR41","doi-asserted-by":"crossref","unstructured":"Weston J, Collobert R, Sinz F, Bottou L, Vapnik V (2006) Inference with the universum. In: Proceedings of the 23rd International Conference on Machine Learning, pp 1009\u20131016","DOI":"10.1145\/1143844.1143971"},{"key":"2402_CR42","doi-asserted-by":"crossref","unstructured":"Xiao Y, Wen J, Liu B (2020) A new multi-task learning method with universum data. Appl Intell:1\u201314","DOI":"10.1007\/s10489-020-01954-3"},{"key":"2402_CR43","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.neucom.2016.04.024","volume":"205","author":"Y Xu","year":"2016","unstructured":"Xu Y (2016) K-nearest neighbor-based weighted multi-class twin support vector machine. Neurocomputing 205:430\u2013438","journal-title":"Neurocomputing"},{"key":"2402_CR44","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2015.06.056","volume":"171","author":"Z Yang","year":"2016","unstructured":"Yang Z, Xu Y (2016) Laplacian twin parametric-margin support vector machine for semi-supervised classification. Neurocomputing 171:325\u2013334","journal-title":"Neurocomputing"},{"key":"2402_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2018.06.018","volume":"84","author":"Z Yang","year":"2018","unstructured":"Yang Z, Xu Y (2018) A safe sample screening rule for laplacian twin parametric-margin support vector machine. Pattern Recogn 84:1\u201312","journal-title":"Pattern Recogn"},{"key":"2402_CR46","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.knosys.2019.01.031","volume":"170","author":"J Zhao","year":"2019","unstructured":"Zhao J, Xu Y, Fujita H (2019) An improved non-parallel universum support vector machine and its safe sample screening rule. Knowl-Based Syst 170:79\u201388","journal-title":"Knowl-Based Syst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02402-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02402-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02402-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T01:27:40Z","timestamp":1725240460000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02402-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,17]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["2402"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02402-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,17]]},"assertion":[{"value":"30 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}