{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:39:29Z","timestamp":1777703969676,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2017,7,11]],"date-time":"2017-07-11T00:00:00Z","timestamp":1499731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,7,21]]},"abstract":"<jats:p>\n                    Data streams with class imbalance occur usually in many real applications. Online sequential learning is one of the effective methods for classifying data stream with class imbalance. This paper proposes a dual-weighted online sequential extreme learning machine (dw-ELM) method to solve it. On the basis of online sequential extreme learning machine, the proposed dw-ELM method analyzes the distribution characteristic of data in view of time and space, and gives an adaptive dual weighting scheme to tune the weights at both the time level and the space level. Extensive experimental evaluations on 10 imbalanced datasets indicate that the proposed dw-ELM method outperforms several comparing methods in terms of\n                    <jats:italic>G<\/jats:italic>\n                    -\n                    <jats:italic>mean<\/jats:italic>\n                    and\n                    <jats:italic>F<\/jats:italic>\n                    -\n                    <jats:italic>measure<\/jats:italic>\n                    metrics. Moreover, the proposed dw-ELM method remains superior classification performance in the presence of highly dynamic class imbalance.\n                  <\/jats:p>","DOI":"10.3233\/jifs-16724","type":"journal-article","created":{"date-parts":[[2017,7,11]],"date-time":"2017-07-11T11:23:22Z","timestamp":1499772202000},"page":"1143-1154","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Dual weighted extreme learning machine for imbalanced data stream classification"],"prefix":"10.1177","volume":"33","author":[{"given":"Yong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian, China"},{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhe","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuezhen","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggong","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,7,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.10.001"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.10.025"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347107"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502529"},{"key":"e_1_3_2_6_2","first-page":"178","article-title":"Robust Neural Network for Novelty Detection on Data Streams","author":"Rusiecki A.","year":"2012","unstructured":"RusieckiA., Robust Neural Network for Novelty Detection on Data Streams, Proceedings of the 11th International Conference on Artificial Intelligence and Soft Computing, 2012, pp. 178\u2013186.","journal-title":"Proceedings of the 11th International Conference on Artificial Intelligence and Soft Computing"},{"key":"e_1_3_2_7_2","first-page":"179","volume-title":"Studies in Classification, Data Analysis, and Knowledge Organization","author":"Salperwyck C.","year":"2015","unstructured":"SalperwyckC., LemaireV. and HueC., Incremental Weighted Naive Bays Classifiers for Data Stream, In Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, 2015, pp. 179\u2013190."},{"key":"e_1_3_2_8_2","first-page":"505","article-title":"The Huller: A Simple and Efficient Online SVM","author":"Bordes A.","year":"2005","unstructured":"BordesA. and BottouL., The Huller: A Simple and Efficient Online SVM, Proceedings of the 16th European Conference on Machine Learning, 2005, pp. 505\u2013512.","journal-title":"Proceedings of the 16th European Conference on Machine Learning"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.880583"},{"key":"e_1_3_2_11_2","first-page":"347","article-title":"Online Learning from Imbalanced Data Streams","author":"Nguyen H.M.","year":"2011","unstructured":"NguyenH.M., CooperE.W. and KameiK., Online Learning from Imbalanced Data Streams, Proceedings of International Conference of Soft Computing and Pattern Recognition, 2011, pp 347\u2013352.","journal-title":"Proceedings of International Conference of Soft Computing and Pattern Recognition"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2160459"},{"key":"e_1_3_2_13_2","first-page":"1","article-title":"An Ensemble based Incremental Learning Framework for Concept Drifts and Class Imbalance","author":"Ditzler G.","year":"2010","unstructured":"DitzlerG. and PolikarR., An Ensemble based Incremental Learning Framework for Concept Drifts and Class Imbalance, Proceedings of the International Joint Conference on Neural Networks, 2010, pp. 1\u20138.","journal-title":"Proceedings of the International Joint Conference on Neural Networks"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-013-9286-9"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.03.075"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(90)90049-Q"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177704472"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2011.2168604"},{"key":"e_1_3_2_19_2","unstructured":"BlakeC.L. and MerzC.J. 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