{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:34:54Z","timestamp":1772120094935,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61877023"],"award-info":[{"award-number":["61877023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CCNU19TD009"],"award-info":[{"award-number":["CCNU19TD009"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s13042-022-01766-6","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T14:16:14Z","timestamp":1674137774000},"page":"2333-2352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving boosting methods with a stable loss function handling outliers"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2968-3445","authenticated-orcid":false,"given":"Wang","family":"Chao","sequence":"first","affiliation":[]},{"given":"Li","family":"Bo","sequence":"additional","affiliation":[]},{"given":"Wang","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Pai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"issue":"428","key":"1766_CR1","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1080\/01621459.1994.10476866","volume":"89","author":"T Hastie","year":"1994","unstructured":"Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89(428):1255\u20131270","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"1766_CR2","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337\u2013407","journal-title":"Ann Stat"},{"key":"1766_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.neucom.2018.02.081","volume":"292","author":"Z Pu","year":"2018","unstructured":"Pu Z, Rao R (2018) Exponential stability criterion of high-order bam neural networks with delays and impulse via fixed point approach. Neurocomputing 292:63\u201371","journal-title":"Neurocomputing"},{"issue":"8","key":"1766_CR4","first-page":"935","volume":"233","author":"Y Gao","year":"2019","unstructured":"Gao Y, Wen J, Peng L (2019) New exponential stability criterion for switched linear systems with average dwell time. Proc Inst Mech Eng Part I J Syst Control Eng 233(8):935\u2013944","journal-title":"Proc Inst Mech Eng Part I J Syst Control Eng"},{"issue":"12","key":"1766_CR5","doi-asserted-by":"publisher","first-page":"4451","DOI":"10.1016\/j.patcog.2012.05.002","volume":"45","author":"J Cao","year":"2012","unstructured":"Cao J, Kwong S, Wang R (2012) A noise-detection based adaboost algorithm for mislabeled data. Pattern Recogn 45(12):4451\u20134465","journal-title":"Pattern Recogn"},{"issue":"7","key":"1766_CR6","first-page":"3069","volume":"29","author":"Z Xiao","year":"2017","unstructured":"Xiao Z, Luo Z, Zhong B, Dang X (2017) Robust and efficient boosting method using the conditional risk. IEEE Trans Neural Netw Learn Syst 29(7):3069\u20133083","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1766_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105705","volume":"195","author":"Z Chen","year":"2020","unstructured":"Chen Z, Duan J, Yang C, Kang L, Qiu G (2020) Smlboost-adopting a soft-margin like strategy in boosting. Knowl-Based Syst 195:105705","journal-title":"Knowl-Based Syst"},{"key":"1766_CR8","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1016\/j.ins.2022.07.155","volume":"609","author":"B Liu","year":"2022","unstructured":"Liu B, Huang R, Xiao Y et al (2022) Adaptive robust adaboost-based twin support vector machine with universum data. Inf Sci 609:1334\u20131352","journal-title":"Inf Sci"},{"key":"1766_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.inffus.2019.08.002","volume":"55","author":"H-J Xing","year":"2020","unstructured":"Xing H-J, Liu W-T (2020) Robust adaboost based ensemble of one-class support vector machines. Inf Fusion 55:45\u201358","journal-title":"Inf Fusion"},{"issue":"1","key":"1766_CR10","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"1766_CR11","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","volume":"7","author":"A Natekin","year":"2013","unstructured":"Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7:21","journal-title":"Front Neurorobot"},{"issue":"4","key":"1766_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367\u2013378","journal-title":"Comput Stat Data Anal"},{"issue":"4","key":"1766_CR13","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1214\/aoms\/1177692459","volume":"43","author":"PJ Huber","year":"1972","unstructured":"Huber PJ (1972) The 1972 wald lecture robust statistics: a review. Ann Math Stat 43(4):1041\u20131067","journal-title":"Ann Math Stat"},{"key":"1766_CR14","doi-asserted-by":"crossref","unstructured":"Wang L, Zheng C, Zhou W et al (2020) A new principle for tuning-free Huber regression. Stat Sin","DOI":"10.5705\/ss.202019.0045"},{"issue":"8","key":"1766_CR15","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.1162\/neco.2007.19.8.2183","volume":"19","author":"T Kanamori","year":"2007","unstructured":"Kanamori T, Takenouchi T, Eguchi S et al (2007) Robust loss functions for boosting. Neural Comput 19(8):2183\u20132244","journal-title":"Neural Comput"},{"issue":"1","key":"1766_CR16","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1198\/000313002753631330","volume":"56","author":"LA Stefanski","year":"2002","unstructured":"Stefanski LA, Boos DD (2002) The calculus of m-estimation. Am Stat 56(1):29\u201338","journal-title":"Am Stat"},{"issue":"2","key":"1766_CR17","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1109\/TITS.2013.2287512","volume":"15","author":"Y Daraghmi","year":"2014","unstructured":"Daraghmi Y, Yi C, Chiang T (2014) Negative binomial additive models for short-term traffic flow forecasting in urban areas. IEEE Trans Intell Transp Syst 15(2):784\u2013793","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"1766_CR18","doi-asserted-by":"publisher","first-page":"4362","DOI":"10.1109\/TPWRS.2017.2669839","volume":"32","author":"J Lv","year":"2017","unstructured":"Lv J, Pawlak M, Annakkage UD (2017) Prediction of the transient stability boundary based on nonparametric additive modeling. IEEE Trans Power Syst 32(6):4362\u20134369","journal-title":"IEEE Trans Power Syst"},{"key":"1766_CR19","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1109\/ACCESS.2018.2805819","volume":"6","author":"P Rana","year":"2018","unstructured":"Rana P, Vilar J, Aneiros G (2018) On the use of functional additive models for electricity demand and price prediction. IEEE Access 6:9603\u20139613","journal-title":"IEEE Access"},{"issue":"2","key":"1766_CR20","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/BF00116037","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197\u2013227","journal-title":"Mach Learn"},{"issue":"2","key":"1766_CR21","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1006\/inco.1995.1136","volume":"121","author":"Y Freund","year":"1995","unstructured":"Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256\u2013285","journal-title":"Inf Comput"},{"issue":"5","key":"1766_CR22","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189\u20131232","journal-title":"Ann Stat"},{"issue":"24","key":"1766_CR23","doi-asserted-by":"publisher","first-page":"6167","DOI":"10.1080\/03610926.2020.1740736","volume":"50","author":"LV Utkin","year":"2021","unstructured":"Utkin LV, Coolen FP (2021) A new boosting-based software reliability growth model. Commun Stat Theory Methods 50(24):6167\u20136194","journal-title":"Commun Stat Theory Methods"},{"issue":"376","key":"1766_CR24","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1080\/01621459.1981.10477729","volume":"76","author":"JH Friedman","year":"1981","unstructured":"Friedman JH, Stuetzle W (1981) Projection pursuit regression. J Am Stat Assoc 76(376):817\u2013823","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"1766_CR25","first-page":"453","volume":"17","author":"A Buja","year":"1989","unstructured":"Buja A, Hastie T, Tibshirani R (1989) Linear smoothers and additive models. Ann Stat 17(2):453\u2013510","journal-title":"Ann Stat"},{"issue":"12","key":"1766_CR26","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1109\/78.258082","volume":"41","author":"SG Mallat","year":"1993","unstructured":"Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397\u20133415","journal-title":"IEEE Trans Signal Process"},{"issue":"2\/3","key":"1766_CR27","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"RE Schapire","year":"1998","unstructured":"Schapire RE, Singer Y (1998) Boostexter: a system for multiclass multi-label text categorization. Mach Learn 39(2\/3):135\u2013168","journal-title":"Mach Learn"},{"issue":"7","key":"1766_CR28","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1162\/089976699300016106","volume":"11","author":"L Breiman","year":"1999","unstructured":"Breiman L (1999) Prediction games and arcing algorithms. Neural Comput 11(7):1493\u20131517","journal-title":"Neural Comput"},{"key":"1766_CR29","unstructured":"Freund Y, Schapire RE et al (1996) Experiments with a new boosting algorithm. In: Icml, vol 96, pp 148\u2013156. Citeseer"},{"issue":"2","key":"1766_CR30","first-page":"929","volume":"23","author":"L Dicker","year":"2013","unstructured":"Dicker L, Huang B, Lin X (2013) Variable selection and estimation with the seamless-l0 penalty. Stat Sin 23(2):929\u2013962","journal-title":"Stat Sin"},{"issue":"4","key":"1766_CR31","first-page":"1595","volume":"27","author":"B Jiang","year":"2017","unstructured":"Jiang B, Wu T-Y, Zheng C et al (2017) Learning summary statistic for approximate Bayesian computation via deep neural network. Stat Sin 27(4):1595\u20131618","journal-title":"Stat Sin"},{"key":"1766_CR32","unstructured":"Ke G, Meng Q, Finley T et al (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30"},{"key":"1766_CR33","unstructured":"Chen T, He T, Benesty M et al (2015) Xgboost: extreme gradient boosting. R package version 0.4-2 1(4):1\u20134"},{"key":"1766_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1766_CR35","unstructured":"Frank A, Asuncion A et al (2011) Uci machine learning repository, 2010, vol 15, p 22. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"1766_CR36","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01766-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01766-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01766-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T10:57:52Z","timestamp":1684148272000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01766-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,19]]},"references-count":36,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["1766"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01766-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1402125\/v1","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-1402125\/v2","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,19]]},"assertion":[{"value":"28 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}