第3期 左鹏玉，等：面对类别不平衡的增量在线序列极限学习机 ·527· [8]DOUZAS G,BACAO F,LAST F.Improving imbalanced [15]BATUWITA R,PALADE V.FSVM-CIL:fuzzy support learning through a heuristic oversampling method based on vector machines for class imbalance learning[J].IEEE k-means and SMOTE[J].Information sciences,2018,465: transactions on fuzzy systems,2010,18(3):558-571. 1-20. [16]DING Shuya,MIRZA B,LIN Zhiping,et al.Kernel based [9]BATUWITA R,PALADE V.Class imbalance learning online learning for imbalance multiclass classification[J]. methods for support vector machines[Ml//HE Haibo,MA Neurocomputing,2017,277:139-148. Yungian.Imbalanced Learning:Foundations,Algorithms, [17]HE H,GARCIA E A.Learning from imbalance data[J]. and Applications.New York:John Wiley Sons,Inc., IEEE transactions on knowledge and data engineering, 2013:145-168. 2009,21(9y:1263-1284. [10]XIA Shixiong,MENG Fanrong,LIU Bing,et al.A Ker- nel Clustering-based possibilistic fuzzy extreme learning 作者简介： machine for class imbalance learning[J].Cognitive com- 左鹏玉，硕士研究生，主要研究方 putation,2015,7(1)74-85. 向为人工智能、模式识别。 [11]ZONG Weiwei,HUANG Guangbin,CHEN Yiqiang. Weighted extreme learning machine for imbalance learn- ing[J].Neurocomputing,2013,101:229-242. [12]MIRZA B,LIN Zhiping,TOH K A.Weighted online se- quential extreme learning machine for class imbalance 周洁，博士研究生，主要研究方向 learning[J].Neural processing letters,2013,38(3): 为人工智能、模式识别、机器学习。 465-486. [13]HUANG Guangbin,ZHOU Hongming,DING Xiaojian, et al.Extreme learning machine for regression and multi- class classification[J].IEEE transactions on systems,man, and cybernetics,part B(cybernetics),2012,42(2): 513-529. 王士同，教授，博土生导师，CCF [14]RAO C R,MITRA S K.Generalized inverse of a matrix 会员，主要研究方向为人工智能、模式 识别。作为第一作者发表学术论文百 and its applications[C]//Proceedings of the Sixth Berke- 余篇。 ley Symposium on Mathematical Statistics and Probabil- ity,Volume 1:Theory of Statistics.Berkeley,:Uni- versity of California Press,1972:601-620.DOUZAS G, BACAO F, LAST F. Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE[J]. Information sciences, 2018, 465: 1–20. [8] BATUWITA R, PALADE V. Class imbalance learning methods for support vector machines[M]//HE Haibo, MA Yunqian. Imbalanced Learning: Foundations, Algorithms, and Applications. New York: John Wiley & Sons, Inc., 2013: 145–168. [9] XIA Shixiong, MENG Fanrong, LIU Bing, et al. A Ker￾nel Clustering-based possibilistic fuzzy extreme learning machine for class imbalance learning[J]. Cognitive com￾putation, 2015, 7(1): 74–85. [10] ZONG Weiwei, HUANG Guangbin, CHEN Yiqiang. Weighted extreme learning machine for imbalance learn￾ing[J]. Neurocomputing, 2013, 101: 229–242. [11] MIRZA B, LIN Zhiping, TOH K A. Weighted online se￾quential extreme learning machine for class imbalance learning[J]. Neural processing letters, 2013, 38(3): 465–486. [12] HUANG Guangbin, ZHOU Hongming, DING Xiaojian, et al. Extreme learning machine for regression and multi￾class classification[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 2012, 42(2): 513–529. [13] RAO C R, MITRA S K. Generalized inverse of a matrix and its applications[C]//Proceedings of the Sixth Berke￾ley Symposium on Mathematical Statistics and Probabil￾ity, Volume 1: Theory of Statistics. Berkeley, : Uni￾versity of California Press, 1972: 601–620. [14] BATUWITA R, PALADE V. FSVM-CIL: fuzzy support vector machines for class imbalance learning[J]. IEEE transactions on fuzzy systems, 2010, 18(3): 558–571. [15] DING Shuya, MIRZA B, LIN Zhiping, et al. Kernel based online learning for imbalance multiclass classification[J]. Neurocomputing, 2017, 277: 139–148. [16] HE H, GARCIA E A. Learning from imbalance data[J]. IEEE transactions on knowledge and data engineering, 2009, 21(9): 1263–1284. [17] 作者简介： 左鹏玉，硕士研究生，主要研究方 向为人工智能、模式识别。 周洁，博士研究生，主要研究方向 为人工智能、模式识别、机器学习。 王士同，教授，博士生导师，CCF 会员，主要研究方向为人工智能、模式 识别。作为第一作者发表学术论文百 余篇。 第 3 期 左鹏玉，等：面对类别不平衡的增量在线序列极限学习机 ·527·