{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T16:01:23Z","timestamp":1793030483618,"version":"4.5.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2011,7,20]],"date-time":"2011-07-20T00:00:00Z","timestamp":1311120000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2013,2]]},"DOI":"10.1007\/s00521-011-0708-1","type":"journal-article","created":{"date-parts":[[2011,7,19]],"date-time":"2011-07-19T04:28:40Z","timestamp":1311049720000},"page":"287-294","source":"Crossref","is-referenced-by-count":14,"title":["Ensemble learning for wind profile prediction with missing values"],"prefix":"10.1007","volume":"22","author":[{"given":"Haibo","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyu","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2011,7,20]]},"reference":[{"key":"708_CR1","unstructured":"Airport station (ASOS\/AWOS). [online], Available: http:\/\/climate.sdstate.edu\/airport\/surface\/archive.asp"},{"key":"708_CR2","unstructured":"Office of Electric Transmission and Distribution (2003) United State Department of Energy, Grid 2030: a national version for electricity second 100\u00a0years"},{"key":"708_CR3","unstructured":"U.S. Department of Energy (2008) The smart grid: an introduction; [online], Available: http:\/\/www.oe.energy.gov\/smartgrid.htm"},{"issue":"8","key":"708_CR4","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1002\/cem.946","volume":"19","author":"F Arteaga","year":"2005","unstructured":"Arteaga F, Ferrer A (2005) Framework for regression-based missing data imputation methods in online mspc. J Chemom 19(8):439\u2013447","journal-title":"J Chemom"},{"issue":"1","key":"708_CR5","first-page":"1","volume":"10","author":"A Balouktsis","year":"1986","unstructured":"Balouktsis A, Tsanakas D, Vachtsevanos G (1986) Stochastic simulation of hourly and daily average wind speed sequences. Wind Eng 10(1):1\u201311","journal-title":"Wind Eng"},{"key":"708_CR6","unstructured":"Beyer HG, Degner T, Haussmann J, Hoffman M, Rujan P (1994) Short term prediction of wind speed and power output of a wind turbine with neural networks. In Proceedings the second European congress on intelligent techniques and soft computing"},{"issue":"2","key":"708_CR7","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"2","key":"708_CR8","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"708_CR9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"5","key":"708_CR10","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1109\/TSMCA.2007.902631","volume":"35","author":"A Farhangfar","year":"2007","unstructured":"Farhangfar A, Kurgan LA, Pedrycz W (2007) A novel framework for imputation of missing values in databases. IEEE Trans Syst Man Cyber Part A Syst 35(5):692\u2013709","journal-title":"IEEE Trans Syst Man Cyber Part A Syst"},{"key":"708_CR11","unstructured":"Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of international conference machine learning, pp 148\u2013156"},{"issue":"1","key":"708_CR12","doi-asserted-by":"crossref","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 online learning and application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"708_CR13","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1999","unstructured":"Haykin S (1999) Neural networks: a comprehensive foundation. 2nd edn. Prentice Hall, Upper Saddle River, New Jersey","edition":"2"},{"key":"708_CR14","unstructured":"He H, Shen X (2007) A ranked subspace learning method for gene expression data classification. In: Proceedings of international conference artificial intelligence, pp 358\u2013364"},{"key":"708_CR15","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TPWRD.2005.852392","volume":"21","author":"H He","year":"2006","unstructured":"He H, Starzyk JA (2006) A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans Power Deliv 21:286\u2013295","journal-title":"IEEE Trans Power Deliv"},{"issue":"8","key":"708_CR16","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho TK (1998) Random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832\u2013844","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"708_CR17","unstructured":"Ho TK (2003) Multiple classifier combination: lessons and next steps. Hybrid Methods Pattern Recognit (2):219\u2013230"},{"key":"708_CR18","doi-asserted-by":"crossref","unstructured":"Ipakchi A, Albuyeh F (2009) Grid of the future. IEEE Power Energy Mag, pp 52\u201362","DOI":"10.1109\/MPE.2008.931384"},{"issue":"1","key":"708_CR19","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Comput 3(1):79\u201387","journal-title":"Neural Comput"},{"issue":"2","key":"708_CR20","first-page":"79","volume":"6","author":"MJ Jordan","year":"1991","unstructured":"Jordan MJ, Jacobs RA (1991) Hierarchical mixtures of experts and the em algorithm. Neural Comput 6(2):79\u201387","journal-title":"Neural Comput"},{"issue":"9","key":"708_CR21","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1016\/0893-6080(95)00014-3","volume":"8","author":"MJ Jordan","year":"1995","unstructured":"Jordan MJ, Xu L (1995) Convergence results for the em approach to mixtures of experts architectures. Neural Netw 8(9):1409\u20131431","journal-title":"Neural Netw"},{"issue":"1","key":"708_CR22","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0038-092X(97)00037-6","volume":"61","author":"L Kamal","year":"1997","unstructured":"Kamal L, Jafri YZ (1997) Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan. Sol Energy 61(1):23\u201332","journal-title":"Sol Energy"},{"key":"708_CR23","unstructured":"Kariniotakis G, Stavrakakis G, Nogaret E (1996) A fuzzy logic and neural network based wind power model. In: Proceedings of the 1996 European Wind energy conference, pp 596\u2013599"},{"issue":"4","key":"708_CR24","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/60.556376","volume":"11","author":"G Kariniotakis","year":"1996","unstructured":"Kariniotakis G, Stavrakakis G, Nogaret E (1996) Wind power forecasting using advanced neural network models. IEEE Trans Energy Conver 11(4):762\u2013767","journal-title":"IEEE Trans Energy Conver"},{"issue":"3","key":"708_CR25","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/34.667881","volume":"20","author":"J Kittler","year":"1998","unstructured":"Kittler J, Hatel M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226\u2013239","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"708_CR26","unstructured":"Krogh A, Vedelsby J (1995) Advances in neural information processing systems, volume 7, chapter Neural network ensembles, cross validation and active learning. MIT Press, pp 231\u2013238"},{"key":"708_CR27","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181\u2013207","journal-title":"Mach Learn"},{"key":"708_CR28","unstructured":"Lam L (2000) International workshop on multiple classifier systems, lecture notes in computer science, volume 1857, chapter classifier combinations: implementations and theoretical issues. Springer, pp 78\u201386"},{"key":"708_CR29","unstructured":"Lin L, Eriksson JT, Vihriala H, Soderlund L (1996) Predicting wind behavior with neural networks. In: Proceedings of the 1996 European Wind energy conference, pp 655\u2013658"},{"key":"708_CR30","doi-asserted-by":"crossref","DOI":"10.1002\/9781119013563","volume-title":"Statistical analysis with missing data","author":"RJA Little","year":"2002","unstructured":"Little RJA, Rubin DB (2002) Statistical analysis with missing data. 2nd edn. Wiley-Interscience, Hoboke","edition":"2"},{"issue":"12","key":"708_CR31","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1109\/32.58771","volume":"15","author":"B Littlewood","year":"1989","unstructured":"Littlewood B, Miller D (1989) Conceptual modeling of coincident failures in multiversion software. IEEE Trans Softw Eng 15(12):1596\u20131614","journal-title":"IEEE Trans Softw Eng"},{"issue":"11","key":"708_CR32","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1109\/32.965340","volume":"27","author":"I Myrtveit","year":"2001","unstructured":"Myrtveit I, Stensrud E, Olsson E (2001) Analyzing data sets with missing data: an empirical evaluation of imputation methods and likelihood-based methods. IEEE Trans Softw Eng 27(11):999\u20131013","journal-title":"IEEE Trans Softw Eng"},{"key":"708_CR33","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1613\/jair.614","volume":"11","author":"D Opitz","year":"1999","unstructured":"Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169\u2013198","journal-title":"J Artif Intell Res"},{"issue":"2","key":"708_CR34","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1214\/ss\/1063994977","volume":"18","author":"DN Politis","year":"2003","unstructured":"Politis DN (2003) The impact of bootstrap methods on time series analysis. Stat Sci 18(2):219\u2013230","journal-title":"Stat Sci"},{"issue":"3\/4","key":"708_CR35","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/095400996116820","volume":"8","author":"B Rosen","year":"1996","unstructured":"Rosen B (1996) Ensemble learning using decorrelated neural networks. Conn Sci 8(3\/4):373\u2013383","journal-title":"Conn Sci"},{"key":"708_CR36","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316696","volume-title":"Multiple imputations for nonresponse in surveys","author":"DB Rubin","year":"1987","unstructured":"Rubin DB (1987) Multiple imputations for nonresponse in surveys. Wiley, New York"},{"issue":"434","key":"708_CR37","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/01621459.1996.10476908","volume":"91","author":"DB Rubin","year":"1996","unstructured":"Rubin DB (1996) Multiple imputation after 18+ years. J Am Stat Assoc 91(434):473\u2013489","journal-title":"J Am Stat Assoc"},{"key":"708_CR38","unstructured":"Sarle WS (1998) Proceedings of JCIS, volume 2, chapter prediction with missing Inputs, pp 399\u2013402"},{"issue":"1","key":"708_CR39","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0960-1481(99)00125-1","volume":"21","author":"A Sfetsos","year":"2000","unstructured":"Sfetsos A (2000) A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21(1):23\u201335","journal-title":"Renewable Energy"},{"key":"708_CR40","unstructured":"Starzyk J, Zhu Z (2002) Software simulation of a self-organizing learning array system. The 6th IASTED international conference artificial intelligence & Soft Comp. (ASC 2002)"},{"key":"708_CR41","unstructured":"Starzyk JA, Zhu Z, He H (2003) Self-organizing learning array and its application to economic and financial problems. In: Proceedings of joint conference information systems"},{"issue":"10","key":"708_CR42","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/32.962560","volume":"27","author":"K Strike","year":"2001","unstructured":"Strike K, Emam KE, Madhavji N (2001) Software cost estimation with incomplete data. IEEE Trans Softw Eng 27(10):890\u2013908","journal-title":"IEEE Trans Softw Eng"},{"key":"708_CR43","volume-title":"Pattern recognition","author":"S Theodoridis","year":"2006","unstructured":"Theodoridis S, Koutroumbas K (2006) Pattern recognition. 3rd edn. Elsevier, Amsterdam","edition":"3"},{"key":"708_CR44","doi-asserted-by":"crossref","unstructured":"Wang H, Fan W, Yu P, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of ACM SIGKDD Conference (SIGKDD\u201903), pp 226\u2013235","DOI":"10.1145\/956750.956778"},{"issue":"2","key":"708_CR45","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"key":"708_CR46","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1016\/j.ins.2006.08.002","volume":"177","author":"Z Zhu","year":"2007","unstructured":"Zhu Z, He H, Starzyk JA, Tseng C (2007) Self-organizing learning array and its application to economic and financial problems. Inform Sci 177:1180\u20131192","journal-title":"Inform Sci"},{"key":"708_CR47","doi-asserted-by":"crossref","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","DOI":"10.1109\/TKDE.2008.239"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-011-0708-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-011-0708-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-011-0708-1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T03:34:02Z","timestamp":1560396842000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-011-0708-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,7,20]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2013,2]]}},"alternative-id":["708"],"URL":"https:\/\/doi.org\/10.1007\/s00521-011-0708-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,7,20]]}}}