{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T21:06:48Z","timestamp":1761599208895,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071475;11671010"],"award-info":[{"award-number":["12071475;11671010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4172035"],"award-info":[{"award-number":["4172035"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10489-020-02024-4","type":"journal-article","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T18:02:48Z","timestamp":1604340168000},"page":"2279-2290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-parameter safe screening rule for hinge-optimal margin distribution machine"],"prefix":"10.1007","volume":"51","author":[{"given":"Mengdan","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7577-4420","authenticated-orcid":false,"given":"Yitian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"issue":"3","key":"2024_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"},{"key":"2024_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995) The nature of statistical learning theory. Springer, New York"},{"key":"2024_CR3","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianin","year":"2000","unstructured":"Cristianin N, Shawe-Taylar J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge"},{"key":"2024_CR4","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, Philadelphia"},{"key":"2024_CR5","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble methods: Foundations and algorithms","author":"Z Zhou","year":"2012","unstructured":"Zhou Z, methods Ensemble (2012) Ensemble methods: Foundations and algorithms. CRC Press, Boca Raton"},{"issue":"5","key":"2024_CR6","first-page":"1651","volume":"26","author":"RE Schapire","year":"1998","unstructured":"Schapire RE, Freund Y, Barlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651\u20131686","journal-title":"Ann Stat"},{"key":"2024_CR7","doi-asserted-by":"crossref","unstructured":"Reyzin L, Schapire RE (2006) How boosting the margin can also boost classifier complexity. In: Proceeding of 23rd international conference on machine learning, Pittsburgh, PA , pp 753\u2013760","DOI":"10.1145\/1143844.1143939"},{"key":"2024_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2013.07.002","volume":"203","author":"W Gao","year":"2013","unstructured":"Gao W, Zhou Z (2013) On the doubt about margin explanation of boosting. Artif Intell 203:1\u201318","journal-title":"Artif Intell"},{"key":"2024_CR9","doi-asserted-by":"crossref","unstructured":"Zhou Z (2014) Large margin distribution learning. In: Proceedings of the 6th IAPR international workshop on artificial neural networks in pattern recognition. Montreal, Canada, pp 1\u201311","DOI":"10.1007\/978-3-319-11656-3_1"},{"key":"2024_CR10","doi-asserted-by":"crossref","unstructured":"Zhang T, Zhou Z (2014) Large margin distribution machine. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery data mining, pp 313\u2013322","DOI":"10.1145\/2623330.2623710"},{"issue":"6","key":"2024_CR11","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1109\/TKDE.2019.2897662","volume":"32","author":"T Zhang","year":"2020","unstructured":"Zhang T, Zhou Z (2020) Optimal margin distribution machine. IEEE Trans Knowl Data Eng 32(6):1143\u20131156","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"2024_CR12","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1109\/TKDE.2016.2535283","volume":"28","author":"Y Zhou","year":"2016","unstructured":"Zhou Y, Zhou Z (2016) Large margin distribution learning with cost interval and unlabeled data. IEEE Trans Knowl Data Eng 28(7):1749\u20131763","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2024_CR13","doi-asserted-by":"crossref","unstructured":"Zhang T, Zhou Z (2018) Optimal margin distribution clustering. In: Proceedings of the 32nd AAAI conference on artificial intelligence, pp 4474\u20134481. New Orleans, LA","DOI":"10.1609\/aaai.v32i1.11737"},{"issue":"3","key":"2024_CR14","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s10994-019-05837-8","volume":"109","author":"Z Tan","year":"2020","unstructured":"Tan Z, Tan P, Jiang Y, Zhou Z (2020) Multi-label optimal margin distribution machine. Mach Learn 109(3):623\u2013642","journal-title":"Mach Learn"},{"key":"2024_CR15","doi-asserted-by":"publisher","first-page":"128043","DOI":"10.1109\/ACCESS.2020.3007834","volume":"8","author":"C Guo","year":"2020","unstructured":"Guo C, Deng H, Chen H (2020) Optimal margin distribution additive machine. IEEE Access 8:128043\u2013128049","journal-title":"IEEE Access"},{"key":"2024_CR16","doi-asserted-by":"publisher","first-page":"74864","DOI":"10.1109\/ACCESS.2020.2988764","volume":"8","author":"T Luan","year":"2020","unstructured":"Luan T, Luo T, Zhuge W (2020) Optimal representative distribution margin machine for multi-instance learning. IEEE Access 8:74864\u201374874","journal-title":"IEEE Access"},{"key":"2024_CR17","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.patrec.2019.05.005","volume":"125","author":"X Zhang","year":"2019","unstructured":"Zhang X, Wang D, Zhou Y (2019) Kernel modified optimal margin distribution machine for imbalanced data classification. Pattern Recogn Lett 125:325\u2013332","journal-title":"Pattern Recogn Lett"},{"key":"2024_CR18","doi-asserted-by":"crossref","unstructured":"Ou G, Wang Y, Pang W, Coghill GM (2017) Large margin distribution machine recursive feature elimination. In: The 4th international conference on systems and informatics (ICSAI), pp 1518\u20131523. Hangzhou, China","DOI":"10.1109\/ICSAI.2017.8248525"},{"key":"2024_CR19","doi-asserted-by":"crossref","unstructured":"Hsieh C, Chang K, Lin C, Keerthi SS, Sundararajan S (2008) A dual coordinate descent method for large-scale linear svm. Proceedings of the 25th International conference on machine learning, pp 408\u2013415, Helsinki, Finland","DOI":"10.1145\/1390156.1390208"},{"key":"2024_CR20","doi-asserted-by":"publisher","first-page":"105441","DOI":"10.1016\/j.knosys.2019.105441","volume":"192","author":"M Mohamad","year":"2020","unstructured":"Mohamad M, Selamat A, Krejcar O, Fujita H, Wu T (2020) An analysis on new hybrid parameter selection model performance over big data set. Knowledge Based Systems 192:105441","journal-title":"Knowledge Based Systems"},{"issue":"4","key":"2024_CR21","first-page":"667","volume":"8","author":"LE Ghaoui","year":"2010","unstructured":"Ghaoui LE, Viallon V, Rabbani T (2010) Safe feature elimination in sparse supervised learning. Pacific Journal of Optimization 8(4):667\u2013698","journal-title":"Pacific Journal of Optimization"},{"key":"2024_CR22","doi-asserted-by":"crossref","unstructured":"Xiang ZJ, Ramadge PJ (2012) Fast lasso screening tests based on correlations. IEEE International conference on acoustics speech and signal processing, pp 2137\u20132140, Kyoto, Japan","DOI":"10.1109\/ICASSP.2012.6288334"},{"key":"2024_CR23","first-page":"1053","volume":"27","author":"J Wang","year":"2014","unstructured":"Wang J, Zhou J, Liu J, Wonka P, Ye J (2014) A safe screening rule for sparse logistic regression. Advances in Neural Information Processing Systems 27:1053\u20131061. Montreal, Canada","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2024_CR24","unstructured":"E Ndiaye, Fercoq O, Gramfort A, Salmon J (2016) Gap safe screening rules for sparse-group-lasso, vol 29. Barcelona, Spain"},{"key":"2024_CR25","unstructured":"Ogawa K, Suzuki Y, Takeuchi I (2013) Safe screening of non-support vectors in pathwise svm computation. In Proceedings of the 30th international conference on machine learning, pp 1382\u20131390, Atlanta USA"},{"key":"2024_CR26","unstructured":"Wang J, Wonka P, Ye J (2014) Scaling svm and least absolute deviations via exact data reduction. In Proceedings of the 31th international conference on machine learning, pp 1912\u20131927, Beijing, China"},{"issue":"2","key":"2024_CR27","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1093\/biomet\/88.2.381","volume":"88","author":"Z Jin","year":"2001","unstructured":"Jin Z, Ying Z, Wei L (2001) A simple resampling method by perturbing the minimand. Biometrika 88 (2):381\u2013390","journal-title":"Biometrika"},{"issue":"1","key":"2024_CR28","doi-asserted-by":"publisher","first-page":"88","DOI":"10.2307\/146316","volume":"27","author":"M Buchinsky","year":"1998","unstructured":"Buchinsky M (1998) Recent advances in quantile regression models. Journal of Human Resources 27(1):88\u2013126","journal-title":"Journal of Human Resources"},{"issue":"5","key":"2024_CR29","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1109\/TNNLS.2017.2688182","volume":"29","author":"X Pan","year":"2018","unstructured":"Pan X, Yang Z, Xu Y, Wang L (2018) Safe screening rules for accelerating twin support vector machine classification. IEEE Transactions on Neural Networks and Learning Systems 29(5): 1876\u20131887","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2024_CR30","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. Knowledge Based Systems 170:79\u201388","journal-title":"Knowledge Based Systems"},{"key":"2024_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2019.05.037","volume":"95","author":"X Pang","year":"2019","unstructured":"Pang X, Pan X, Xu Y (2019) Multi-parameter safe sample elimination rule for accelerating nonlinear multi-class support vector machines. Pattern Recogn 95:1\u201311","journal-title":"Pattern Recogn"},{"issue":"15","key":"2024_CR32","doi-asserted-by":"publisher","first-page":"4043","DOI":"10.1109\/TSP.2019.2924580","volume":"67","author":"H Wang","year":"2019","unstructured":"Wang H, Pan X, Xu Y (2019) Simultaneous safe feature and sample elimination for sparse support vector regression. IEEE Trans Signal Process 67(15):4043\u20134054","journal-title":"IEEE Trans Signal Process"},{"key":"2024_CR33","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, New York"},{"issue":"1","key":"2024_CR34","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/BF02844417","volume":"33","author":"D Preiss","year":"1984","unstructured":"Preiss D (1984) Gateaux differentiable functions are somewhere Frechet differentiable. Rendiconti del Circolo Matematico di Palermo 33(1):122\u2013133","journal-title":"Rendiconti del Circolo Matematico di Palermo"},{"key":"2024_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68407-9","volume-title":"Foundations of optimization","author":"O G\u00fcler","year":"2010","unstructured":"G\u00fcler O (2010) Foundations of optimization. Springer, New York"},{"key":"2024_CR36","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.compbiomed.2017.01.001","volume":"81","author":"F Khozeimeh","year":"2017","unstructured":"Khozeimeh F, Alizadehsani R, Roshanzamir M (2017) An expert system for selecting wart treatment method. Comput Biol Med 81:167\u2013175","journal-title":"Comput Biol Med"},{"issue":"2","key":"2024_CR37","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s11634-014-0177-3","volume":"9","author":"J Baudry","year":"2015","unstructured":"Baudry J, Cardoso M, Celeux G (2015) Enhancing the selection of a model-based clustering with external categorical variables. ADAC 9(2):177\u2013196","journal-title":"ADAC"},{"issue":"2","key":"2024_CR38","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5121\/ijdms.2011.3207","volume":"3","author":"BV Ramana","year":"2011","unstructured":"Ramana BV, Babu MSP, Venkateswarlu NB (2011) A critical study of selected classification algorithms for liver disease diagnosis. International Journal of Database Management Systems 3(2):101\u2013114","journal-title":"International Journal of Database Management Systems"},{"issue":"11","key":"2024_CR39","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1118\/1.2786864","volume":"34","author":"M Elter","year":"2007","unstructured":"Elter M, Schulz-Wendtland R, Wittenberg T (2007) The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Med Phys 34(11):4164\u20134172","journal-title":"Med Phys"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-02024-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-020-02024-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-02024-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T06:48:04Z","timestamp":1669445284000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-020-02024-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,2]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["2024"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-02024-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,11,2]]},"assertion":[{"value":"13 October 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}