{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:25:10Z","timestamp":1740108310123,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T00:00:00Z","timestamp":1545177600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Key project of national key R&D project","award":["2017YFC17003303"],"award-info":[{"award-number":["2017YFC17003303"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402387","61402390"],"award-info":[{"award-number":["61402387","61402390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2017J01773","2018J01555"],"award-info":[{"award-number":["2017J01773","2018J01555"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the research program of normal university","award":["2016Z06","2016Z03"],"award-info":[{"award-number":["2016Z06","2016Z03"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s00607-018-0688-4","type":"journal-article","created":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T05:58:45Z","timestamp":1545199125000},"page":"531-545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Effective learning model of user classification based on ensemble learning algorithms"],"prefix":"10.1007","volume":"101","author":[{"given":"Qunsheng","family":"Ruan","sequence":"first","affiliation":[]},{"given":"Qingfeng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yingdong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiling","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fengyu","family":"Miao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,12,19]]},"reference":[{"key":"688_CR1","first-page":"115","volume":"31","author":"SG Zhao","year":"2014","unstructured":"Zhao SG (2014) High conversions ratio user portrait of social media: deep investigation and research based 500 users. Mod Med J Commun Univ China 31:115\u2013120","journal-title":"Mod Med J Commun Univ China"},{"unstructured":"Customer portrait created by China grid client service central based on Big Data. \n                    http:\/\/www.chinapower.com.cn\/dwzhxw\/20160504\/23472.html[DB\/OL]\n                    \n                  . Accessed 07 Oct 2017","key":"688_CR2"},{"key":"688_CR3","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.eswa.2017.04.013","volume":"83","author":"L Lin","year":"2017","unstructured":"Lin L, Wang F, Xie XL (2017) Random forests-based extreme learning machine ensemble for multi-regime time series prediction. Expert Syst Appl 83:164\u2013176","journal-title":"Expert Syst Appl"},{"key":"688_CR4","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neucom.2016.11.086","volume":"268","author":"FV Isaac","year":"2017","unstructured":"Isaac FV, Elena HP, Diego AE (2017) Combining machine learning models for the automatic detection of eeg arousals. Neurocomputing 268:100\u2013108","journal-title":"Neurocomputing"},{"key":"688_CR5","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1016\/j.scitotenv.2018.02.233","volume":"630","author":"M Janik","year":"2018","unstructured":"Janik M, Bossew P, Kurihara O (2018) Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data. Total Environ 630:1155\u20131167","journal-title":"Total Environ"},{"key":"688_CR6","first-page":"341","volume":"5","author":"M Rory","year":"2017","unstructured":"Rory M, Eibe F (2017) Accelerating the XGBoost algorithm using GPU computing. Peer J 5:341\u2013345","journal-title":"Peer J"},{"doi-asserted-by":"crossref","unstructured":"Qiao Y, Zhang HP, Yu M. Sina-Weibo (2016) Spammer detection with GBDT, social media processing. In: 5th National conference on social media processing, 29\u201330 Oct, Nanchang, China","key":"688_CR7","DOI":"10.1007\/978-981-10-2993-6_19"},{"key":"688_CR8","first-page":"1","volume":"99","author":"XS Zhang","year":"2016","unstructured":"Zhang XS, Zhuang Y, Wang W (2016) Transfer boosting with synthetic instances for class imbalanced object recognition. IEEE Trans Cybern 99:1\u201314","journal-title":"IEEE Trans Cybern"},{"key":"688_CR9","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.3390\/s17102376","volume":"17","author":"Y Luo","year":"2017","unstructured":"Luo Y, Ye WB, Zhao XJ (2017) Classification of data from electronic nose using gradient tree boosting algorithm. Sensors 17:2376","journal-title":"Sensors"},{"key":"688_CR10","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.apenergy.2016.08.096","volume":"183","author":"J Ma","year":"2016","unstructured":"Ma J, Cheng CP (2016) Identifying the influential features on the regional energy use intensity of residential buildings based on random forests. Appl Energy 183:193\u2013201","journal-title":"Appl Energy"},{"key":"688_CR11","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.chemolab.2016.07.001","volume":"157","author":"TL Zhang","year":"2016","unstructured":"Zhang TL, Xia DH, Tang HS (2016) Classification of steel samples by laser-induced breakdown spectroscopy and random rorest. Chemometr Intell Lab Syst 157:196\u2013201","journal-title":"Chemometr Intell Lab Syst"},{"key":"688_CR12","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3847\/0004-637X\/832\/2\/123","volume":"832","author":"D Tamayo","year":"2016","unstructured":"Tamayo D, Silburt A, Valencia D (2016) A machine learns to predict the stability of tightly packed planetary systems. Astrophys J Lett 832:123\u2013132","journal-title":"Astrophys J Lett"},{"key":"688_CR13","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.jtbi.2017.09.018","volume":"435","author":"ES Sankari","year":"2017","unstructured":"Sankari ES, Manimegalai D (2017) Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets. J Theor Biol 435:208\u20132017","journal-title":"J Theor Biol"},{"key":"688_CR14","first-page":"3390","volume":"17","author":"J Hyoseon","year":"2017","unstructured":"Hyoseon J, Woongwoo L, Hyeyoung P (2017) Automatic classification of tremor severity in Parkinson\u2019s disease using a wearable device. Sensors 17:3390","journal-title":"Sensors"},{"key":"688_CR15","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.jcis.2018.03.053","volume":"522","author":"S Kulju","year":"2018","unstructured":"Kulju S, Riegger L, Koltay P, Mattila K, Hyvaluoma J (2018) Fluid flow simulations meet high-speed video:\u00a0computer\u00a0vision\u00a0comparison of droplet dynamics. J Colloid Interface Sci 522:45\u201356","journal-title":"J Colloid Interface Sci"},{"key":"688_CR16","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO (2002) Synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"unstructured":"Han H, Wang WY, Mao BH (2014) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: 18th International conference on advances in intelligent computing, 5\u20137 May, Tokyo, Japan","key":"688_CR17"},{"key":"688_CR18","doi-asserted-by":"publisher","first-page":"3456","DOI":"10.1016\/j.neucom.2011.06.010","volume":"74","author":"M Gao","year":"2011","unstructured":"Gao M, Hong X, Chen S (2011) A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems. Neurocomputing 74:3456\u20133466","journal-title":"Neurocomputing"},{"key":"688_CR19","first-page":"852","volume":"79","author":"FC Davila","year":"2016","unstructured":"Davila FC, Renatao DM (2016) A bee-inspired data clustering approach to design RBF neural network classifiers. Neurocomputing 79:852\u2013863","journal-title":"Neurocomputing"},{"key":"688_CR20","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.knosys.2014.01.020","volume":"59","author":"YC Xiao","year":"2014","unstructured":"Xiao YC, Wang HG, Zhang L (2014) Two methods of selecting gaussian kernel parameters for one-class SVM and their application to fault detection. Knowl Based Syst 59:75\u201384","journal-title":"Knowl Based Syst"},{"key":"688_CR21","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1007\/s10664-015-9401-9","volume":"21","author":"L Jonson","year":"2016","unstructured":"Jonson L, Borg M, Broman D (2016) Automated bug assignment: ensemble-based machine learning in large scale industrial context. Empir Softw Eng 21:1533\u20131538","journal-title":"Empir Softw Eng"},{"key":"688_CR22","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.jmva.2014.04.012","volume":"131","author":"OE Dakkak","year":"2014","unstructured":"Dakkak OE, Peccati G, Prunster L (2014) Exchangeable Hoeffding decompositions over finite sets: a combinatorial characterization and counterexamples. J Multivar Anal 131:51\u201364","journal-title":"J Multivar Anal"},{"key":"688_CR23","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Freidman","year":"2001","unstructured":"Freidman J, Jastoe T, Tibshirani T (2001) Additive logistic regression: astatistical view of boosting. Ann Stat 28:337\u2013340","journal-title":"Ann Stat"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-018-0688-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00607-018-0688-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-018-0688-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,18]],"date-time":"2019-12-18T19:21:51Z","timestamp":1576696911000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00607-018-0688-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,19]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["688"],"URL":"https:\/\/doi.org\/10.1007\/s00607-018-0688-4","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"type":"print","value":"0010-485X"},{"type":"electronic","value":"1436-5057"}],"subject":[],"published":{"date-parts":[[2018,12,19]]},"assertion":[{"value":"20 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}