{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:58Z","timestamp":1740108058930,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T00:00:00Z","timestamp":1561680000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T00:00:00Z","timestamp":1561680000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61672227"],"award-info":[{"award-number":["61672227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation of China for Distinguished Young Scholars","award":["61725301"],"award-info":[{"award-number":["61725301"]}]},{"name":"Shanghai Education Development Foundation and Shanghai Municipal Education Commission","award":["61806078"],"award-info":[{"award-number":["61806078"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00521-019-04306-6","type":"journal-article","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T17:02:35Z","timestamp":1561741355000},"page":"8245-8264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-matrices entropy discriminant ensemble learning for imbalanced problem"],"prefix":"10.1007","volume":"32","author":[{"given":"Zhe","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaozhi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwen","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenli","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongdong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,28]]},"reference":[{"issue":"2","key":"4306_CR1","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2014","unstructured":"Barua S, Islam MM, Yao X, Murase K (2014) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405\u2013425","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"4306_CR2","first-page":"582","volume":"57","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman JH, Olshen R, Stone CJ (1984) Classification and regression trees. Wadsworth Brooks 57(1):582\u2013588","journal-title":"Wadsworth Brooks"},{"key":"4306_CR3","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-smote: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Pacific-Asia conference on advances in knowledge discovery and data mining, pp 475\u2013482","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"4306_CR4","unstructured":"Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: International joint conference on artificial intelligence, pp 708\u2013713"},{"key":"4306_CR5","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.knosys.2018.02.031","volume":"150","author":"C Cao","year":"2018","unstructured":"Cao C, Wang Z (2018) Imcstacking: cost-sensitive stacking learning with feature inverse mapping for imbalanced problems. Knowl Based Syst 150:27\u201337","journal-title":"Knowl Based Syst"},{"issue":"6","key":"4306_CR6","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/TNNLS.2013.2246188","volume":"24","author":"CL Castro","year":"2013","unstructured":"Castro CL, Braga AP (2013) Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data. IEEE Trans Neural Netw Learn Syst 24(6):888\u2013899","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4306_CR7","unstructured":"Chan P, Stolfo S (1998) Toward scalable learning with non-uniform class and cost distributions. In: International conference on knowledge discovery and data mining, pp 164\u2013168"},{"key":"4306_CR8","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, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"4306_CR9","first-page":"1","volume-title":"Using random forest to learn imbalanced data","author":"C Chao","year":"2004","unstructured":"Chao C, Andy L, Leo B et al (2004) Using random forest to learn imbalanced data, vol 110. University of California, Berkeley, pp 1\u201312"},{"issue":"5","key":"4306_CR10","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1016\/j.patcog.2006.09.001","volume":"40","author":"S Chen","year":"2007","unstructured":"Chen S, Wang Z, Tian Y (2007) Matrix-pattern-oriented Ho\u2013Kashyap classifier with regularization learning. Pattern Recognit 40(5):1533\u20131543","journal-title":"Pattern Recognit"},{"key":"4306_CR11","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.ins.2014.03.117","volume":"279","author":"Y Chen","year":"2014","unstructured":"Chen Y, Wu K, Chen X, Tang C, Zhu Q (2014) An entropy-based uncertainty measurement approach in neighborhood systems. Inf Sci 279:239\u2013250","journal-title":"Inf Sci"},{"issue":"1","key":"4306_CR12","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1\u201330","journal-title":"J Mach Learn Res"},{"key":"4306_CR13","unstructured":"Drummond C, Holte RC (2000) Exploiting the cost (in)sensitivity of decision tree splitting criteria. In: International conference on machine learning, pp 1\u20132"},{"key":"4306_CR14","volume-title":"Pattern classification","author":"Richard O Duda","year":"2001","unstructured":"Duda Richard O, Hart Peter E, Stork David G (2001) Pattern classification, second edn. Wiley, New York","edition":"second"},{"key":"4306_CR15","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.knosys.2016.09.032","volume":"115","author":"Q Fan","year":"2017","unstructured":"Fan Q, Wang Z, Li DD, Gao DQ, Zha HY (2017) Entropy-based fuzzy support vector machine for imbalanced datasets. Knowl Based Syst 115:87\u201399","journal-title":"Knowl Based Syst"},{"key":"4306_CR16","unstructured":"Fan W, Stolfo SJ, Zhang J, Chan PK (1999) Adacost: misclassification cost-sensitive boosting. In: International conference on machine learning, pp 97\u2013105"},{"key":"4306_CR17","volume-title":"Pattern recognition with support vector machines","author":"G Fumera","year":"2002","unstructured":"Fumera G, Roli F (2002) Support vector machines with embedded reject option. In: Lee S-W, Verri A (eds) Pattern recognition with support vector machines. Springer, Berlin"},{"issue":"4","key":"4306_CR18","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern C (Appl Rev) 42(4):463\u2013484","journal-title":"IEEE Trans Syst Man Cybern C (Appl Rev)"},{"issue":"3","key":"4306_CR19","first-page":"176","volume":"49","author":"HX Guo","year":"2016","unstructured":"Guo HX, Li YJ, Li Y, Liu X, Li J (2016) BPSO-AdaBoost-KNN ensemble learning algorithm for multi-class imbalanced data classification. Eng Appl Artif Intell 49(3):176\u2013193","journal-title":"Eng Appl Artif Intell"},{"key":"4306_CR20","first-page":"878","volume":"2005","author":"H Han","year":"2005","unstructured":"Han H, Wang WY, Mao BH (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. Int Conf Intell Comput 2005:878\u2013887","journal-title":"Int Conf Intell Comput"},{"issue":"9","key":"4306_CR21","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"HB He","year":"2009","unstructured":"He HB, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"4306_CR22","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1007730.1007737","volume":"6","author":"T Jo","year":"2004","unstructured":"Jo T, Japkowicz N (2004) Class imbalances versus small disjuncts. ACM SIGKDD Explor Newsl 6(1):40\u201349","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"4","key":"4306_CR23","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Prog Artif Intell 5(4):221\u2013232","journal-title":"Prog Artif Intell"},{"key":"4306_CR24","unstructured":"Kukar M, Kononenko I et al (1998) Cost-sensitive learning with neural networks. In: European conference on artificial intelligence, pp 445\u2013449"},{"key":"4306_CR25","doi-asserted-by":"crossref","unstructured":"Kwok JT (1999) Moderating the outputs of support vector machine classifiers. In: Proceedings of the international joint conference on neural networks (IJCNN\u201999), pp 943\u2013948","DOI":"10.1109\/IJCNN.1999.831080"},{"issue":"5","key":"4306_CR26","first-page":"1","volume":"11","author":"Q Li","year":"2016","unstructured":"Li Q, Li G, Niu WJ, Cao Y, Chang Lg, Tan JL, Guo L (2016) Boosting imbalanced data learning with wiener process oversampling. Front Comput Sci 11(5):1\u201316","journal-title":"Front Comput Sci"},{"key":"4306_CR27","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988"},{"issue":"2","key":"4306_CR28","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","volume":"39","author":"XY Liu","year":"2009","unstructured":"Liu XY, Wu JX, 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)"},{"key":"4306_CR29","unstructured":"Maloof MA (2003) Learning when data sets are imbalanced and when costs are unequal and unknown. In: International conference on machine learning-2003 workshop on learning from imbalanced data sets II, pp 1\u20132"},{"key":"4306_CR30","unstructured":"Masnadi-Shirazi H, Vasconcelos N, Iranmehr A (2012) Cost-sensitive support vector machines. arXiv preprint \n                  arXiv:1212.0975"},{"issue":"5270","key":"4306_CR31","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1126\/science.272.5270.1905","volume":"272","author":"T Poggio","year":"1996","unstructured":"Poggio T (1996) Image representations for visual learning. Science 272(5270):1905\u20131909","journal-title":"Science"},{"issue":"1","key":"4306_CR32","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2010","unstructured":"Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern A Syst Hum 40(1):185\u2013197","journal-title":"IEEE Trans Syst Man Cybern A Syst Hum"},{"issue":"1","key":"4306_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/584091.584093","volume":"5","author":"CE Shannon","year":"2001","unstructured":"Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5(1):3\u201355","journal-title":"ACM SIGMOBILE Mob Comput Commun Rev"},{"issue":"2","key":"4306_CR34","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s11704-016-5306-z","volume":"12","author":"B Sun","year":"2018","unstructured":"Sun B, Chen HY, Wang JD, Xie H (2018) Evolutionary under-sampling based bagging ensemble method for imbalanced data classification. Front Comput Sci 12(2):331\u2013350","journal-title":"Front Comput Sci"},{"issue":"7\u20138","key":"4306_CR35","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","volume":"23","author":"SL Sun","year":"2013","unstructured":"Sun SL (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7\u20138):2031\u20132038","journal-title":"Neural Comput Appl"},{"issue":"7","key":"4306_CR36","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","volume":"23","author":"Shiliang Sun","year":"2013","unstructured":"Sun Shiliang (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7):2031\u20132038","journal-title":"Neural Comput Appl"},{"issue":"4","key":"4306_CR37","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1142\/S0218001409007326","volume":"23","author":"Y Sun","year":"2009","unstructured":"Sun Y, Wong AKC, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(4):687\u2013719","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"3","key":"4306_CR38","first-page":"11","volume":"2017","author":"Q Wang","year":"2017","unstructured":"Wang Q, Luo ZH, Huang JC, Feng YH, Liu Z (2017) A novel ensemble method for imbalanced data learning: bagging of extrapolation-smote svm. Comput Intell Neurosci 2017(3):11","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"4306_CR39","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s11063-007-9041-1","volume":"26","author":"Z Wang","year":"2007","unstructured":"Wang Z, Chen S (2007) New least squares support vector machines based on matrix patterns. Neural Process Lett 26(1):41\u201356","journal-title":"Neural Process Lett"},{"issue":"10","key":"4306_CR40","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1016\/j.patcog.2011.04.002","volume":"44","author":"Z Wang","year":"2011","unstructured":"Wang Z, Chen SC, Gao DQ (2011) A novel multi-view learning developed from single-view patterns. Pattern Recognit 44(10):2395\u20132413","journal-title":"Pattern Recognit"},{"key":"4306_CR41","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.knosys.2016.10.021","volume":"116","author":"Z Wang","year":"2017","unstructured":"Wang Z, Zhang GW, Li DD, Zhu YJ, Cao CJ (2017) Locality sensitive discriminant matrixized learning machine. Knowl Based Syst 116:13\u201325","journal-title":"Knowl Based Syst"},{"issue":"4","key":"4306_CR42","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1162\/NECO_a_00815","volume":"28","author":"K Wimalawarne","year":"2016","unstructured":"Wimalawarne K, Tomioka R, Sugiyama M (2016) Theoretical and experimental analyses of tensor-based regression and classification. Neural Comput 28(4):686\u2013715","journal-title":"Neural Comput"},{"issue":"1","key":"4306_CR43","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.patcog.2008.07.010","volume":"42","author":"H Xue","year":"2009","unstructured":"Xue H, Chen S, Yang Q (2009) Discriminatively regularized least-squares classification. Pattern Recognit 42(1):93\u2013104","journal-title":"Pattern Recognit"},{"issue":"10","key":"4306_CR44","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/TKDE.2018.2808953","volume":"30","author":"H Zhang","year":"2018","unstructured":"Zhang H, Wang S, Zhao M, Xu X, Ye Y (2018) Locality reconstruction models for book representation. IEEE Trans Knowl Data Eng 30(10):1873\u20131886","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"4306_CR45","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TKDE.2006.17","volume":"18","author":"ZH Zhou","year":"2006","unstructured":"Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63\u201377","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4306_CR46","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.knosys.2015.09.015","volume":"90","author":"YJ Zhu","year":"2015","unstructured":"Zhu YJ, Wang Z, Gao DQ (2015) Gravitational fixed radius nearest neighbor for imbalanced problem. Knowl Based Syst 90:224\u2013238","journal-title":"Knowl Based Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04306-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04306-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04306-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T03:25:57Z","timestamp":1593228357000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04306-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,28]]},"references-count":46,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4306"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04306-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2019,6,28]]},"assertion":[{"value":"16 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors of this manuscript state that there are no conflicts of interests between this manuscript and other published works.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}