{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:31Z","timestamp":1760597971382,"version":"3.37.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"crossref","award":["2019KRM095"],"award-info":[{"award-number":["2019KRM095"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Plan Project of Shangluo of China","award":["SK2019-84"],"award-info":[{"award-number":["SK2019-84"]}]},{"name":"Science and Technology Research Project of Shangluo University of China","award":["18SKY014"],"award-info":[{"award-number":["18SKY014"]}]},{"name":"Science and Technology Innovation Team Building Project of Shangluo University of China","award":["18SCX002"],"award-info":[{"award-number":["18SCX002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s11227-020-03374-z","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T22:02:27Z","timestamp":1594159347000},"page":"2875-2896","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Network anomaly detection based on selective ensemble algorithm"],"prefix":"10.1007","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6417-3600","authenticated-orcid":false,"given":"Hongle","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"issue":"20","key":"3374_CR1","first-page":"178","volume":"35","author":"LX Duan","year":"2016","unstructured":"Duan LX, Guo H, Wang JJ (2016) A mechanical fault severity identification method under unbalanced datasets. J Vib Shock China 35(20):178\u2013182","journal-title":"J Vib Shock China"},{"key":"3374_CR2","doi-asserted-by":"crossref","unstructured":"Du H, Teng S, Zhang L (2016) Support vector machine based on dynamic density equalization. In: Human Centered Computing 2nd International Conference 2016, Lecture Notes in Computer Science(LNCS), vol 9567. Springer Verlag, Berlin, pp 58\u201369","DOI":"10.1007\/978-3-319-31854-7_6"},{"issue":"6","key":"3374_CR3","doi-asserted-by":"publisher","first-page":"3943","DOI":"10.1007\/s00500-019-04165-2","volume":"24","author":"G Liu","year":"2020","unstructured":"Liu G, Chen Z, Zhuang Z, Guo W, Chen G (2020) A unified algorithm based on HTS and self-adapting PSO for the construction of octagonal and rectilinear SMT. Soft Comput 24(6):3943\u20133961. https:\/\/doi.org\/10.1007\/s00500-019-04165-2","journal-title":"Soft Comput"},{"key":"3374_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.03.003","author":"J Wang","year":"2018","unstructured":"Wang J, Zhang XM, Lin Y et al (2018) Event-triggered dissipative control for networked stochastic systems under non-uniform sampling. Inf Sci. https:\/\/doi.org\/10.1016\/j.ins.2018.03.003","journal-title":"Inf Sci"},{"issue":"1","key":"3374_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neucom.2016.02.006","volume":"193","author":"C Jian","year":"2016","unstructured":"Jian C, Gao J, Ao Y (2016) A new sampling method for classifying imbalanced data based on support vector machine ensemble. Neurocomputing 193(1):115\u2013122","journal-title":"Neurocomputing"},{"issue":"11","key":"3374_CR6","first-page":"201","volume":"36","author":"YH Li","year":"2015","unstructured":"Li YH, Lou XG, Qin YK et al (2015) RMPCM: network-wide anomaly detection method based on robust multivariate probabilistic calibration model. J Commun Chin 36(11):201\u2013212 (in Chinese)","journal-title":"J Commun Chin"},{"key":"3374_CR7","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ifs.2017.0495","author":"J Zou","year":"2018","unstructured":"Zou J, Dong L, Wu W (2018) New algorithms for the unbalanced generalized birthday problem. IET Inf Secur. https:\/\/doi.org\/10.1049\/iet-ifs.2017.0495","journal-title":"IET Inf Secur"},{"issue":"11","key":"3374_CR8","first-page":"2089","volume":"30","author":"M Han","year":"2015","unstructured":"Han M, Lu F (2015) Selective ensemble of extreme learning machine with kernels based on mutual information. Control Decis 30(11):2089\u20132092 (in Chinese)","journal-title":"Control Decis"},{"key":"3374_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.neunet.2014.05.014","volume":"57","author":"S Zhong","year":"2014","unstructured":"Zhong S, Chen T, He F et al (2014) Fast Gaussian kernel learning for classification tasks based on specially structured global optimization. Neural Networks 57:51\u201362","journal-title":"Neural Networks"},{"issue":"10","key":"3374_CR10","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1002\/sec.1026","volume":"8","author":"WZ Guo","year":"2015","unstructured":"Guo WZ, Chen JY, Chen GL et al (2015) Trust dynamic task allocation algorithm with Nash equilibrium for heterogeneous wireless sensor network. Secur Commun Networks 8(10):1865\u20131877","journal-title":"Secur Commun Networks"},{"issue":"3","key":"3374_CR11","first-page":"1827016","volume":"2017","author":"Q Wang","year":"2017","unstructured":"Wang Q, Luo ZH, Huang JC et al (2017) A novel ensemble method for imbalanced data learning: bagging of extrapolation-SMOTE SVM. Comput Intell Neurosci 2017(3):1827016","journal-title":"Comput Intell Neurosci"},{"issue":"7","key":"3374_CR12","first-page":"869","volume":"9","author":"HL Du","year":"2016","unstructured":"Du HL (2016) Algorithm for imbalanced dataset based on K-nearest neighbor in kernel space. J Front Comput Sci Technol 9(7):869\u2013876 (in Chinese)","journal-title":"J Front Comput Sci Technol"},{"issue":"7","key":"3374_CR13","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1109\/TKDE.2016.2535283","volume":"28","author":"YH Zhou","year":"2016","unstructured":"Zhou YH, Zhou ZH (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"},{"issue":"1","key":"3374_CR14","doi-asserted-by":"publisher","first-page":"e0146116","DOI":"10.1371\/journal.pone.0146116","volume":"11","author":"MN Haque","year":"2016","unstructured":"Haque MN, Noman N, Berretta R et al (2016) Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data classification. PLoS ONE 11(1):e0146116","journal-title":"PLoS ONE"},{"issue":"5","key":"3374_CR15","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2015","unstructured":"Wang S, Minku LL, Yao X (2015) Resampling-based ensemble methods for online class imbalance learning. IEEE Trans Knowl Data Eng 27(5):1356\u20131368","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"3374_CR16","first-page":"1039","volume":"37","author":"JX Zhou","year":"2000","unstructured":"Zhou JX, Zhou ZH, Shen XH et al (2000) A selective constructing approach to neural network ensemble. J Calcul Res Dev 37(9):1039\u20131044","journal-title":"J Calcul Res Dev"},{"issue":"1\u20132","key":"3374_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","volume":"137","author":"ZH Zhou","year":"2002","unstructured":"Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1\u20132):239\u2013263","journal-title":"Artif Intell"},{"issue":"4","key":"3374_CR18","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1109\/TCYB.2018.2877663","volume":"50","author":"ZH Zhu","year":"2020","unstructured":"Zhu ZH, Wang Z, Li DD et al (2020) Geometric structural ensemble learning for imbalanced problems. IEEE Transac Cyber 50(4):1617\u20131629","journal-title":"IEEE Transac Cyber"},{"issue":"C","key":"3374_CR19","first-page":"176","volume":"49","author":"H Guo","year":"2016","unstructured":"Guo H, Li Y, Li Y et al (2016) BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification. Eng Appl Artif Intell 49(C):176\u2013193","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"3374_CR20","doi-asserted-by":"publisher","first-page":"201","DOI":"10.25103\/jestr.095.31","volume":"9","author":"SP Potharaju","year":"2017","unstructured":"Potharaju SP, Sreedevi M (2017) Ensembled rule based classification algorithms for predicting imbalanced kidney disease data. J Eng Sci Technol Rev 9(5):201\u2013207","journal-title":"J Eng Sci Technol Rev"},{"issue":"11","key":"3374_CR21","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TCYB.2014.2372060","volume":"45","author":"WWY Ng","year":"2017","unstructured":"Ng WWY, Hu J, Yeung DSS et al (2017) Diversified sensitivity-based undersampling for imbalance classification problems. IEEE Trans Cybern 45(11):2402\u20132412","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"3374_CR22","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","volume":"39","author":"XY Liu","year":"2009","unstructured":"Liu XY, Wu J, Zhou ZH (2009) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern B Cybern 39(2):539\u2013550","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"issue":"3","key":"3374_CR23","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s13042-015-0478-7","volume":"8","author":"J Zhai","year":"2017","unstructured":"Zhai J, Zhang S, Wang C (2017) The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers. Int J Mach Learn Cyber 8(3):1009\u20131017","journal-title":"Int J Mach Learn Cyber"},{"issue":"4","key":"3374_CR24","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1109\/TCBB.2014.2306838","volume":"11","author":"H Yu","year":"2014","unstructured":"Yu H, Ni J (2014) An improved ensemble learning method for classifying high-dimensional and imbalanced biomedicine data. IEEE\/ACM Trans Comput Biol Bioinf 11(4):1339\u20131347","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"8","key":"3374_CR25","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3374_CR26","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.knosys.2014.03.022","volume":"64","author":"Y Zhang","year":"2014","unstructured":"Zhang Y, Yang A, Xiong C et al (2014) Feature selection using data envelopment analysis. Knowl-Based Syst 64:70\u201380","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"3374_CR27","doi-asserted-by":"publisher","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(2):181\u2013207","journal-title":"Mach Learn"},{"issue":"5","key":"3374_CR28","first-page":"787","volume":"40","author":"XL Tao","year":"2018","unstructured":"Tao XL, Kang RN, Liu LY (2018) A parallel multi-classifier fusion approach based on selective ensemble. Comput Eng Sci China 40(5):787\u2013792","journal-title":"Comput Eng Sci China"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03374-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-020-03374-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03374-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:36:22Z","timestamp":1625614582000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-020-03374-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["3374"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03374-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2020,7,7]]},"assertion":[{"value":"7 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}