[1] WIDMER G,KUBAT M.Learning in the presence of concept drift and hidden contexts[J].Machine Learning,1996,(23):69-101.
[2] HOENS T R,POLIKAR R,et al.Learning from streaming data with concept drift and imbalance:an overview[J].Progress in Artificial Intelligence,2012,1(1):89-101.
[3] 文益民,强保华,等.概念漂移数据流分类研究综述[J].智能系统学报,2013,8(2):95-104. WEN Yi-min,QIANG Bao-hua,et al.A survey of the classification of data streams with concept drift [J].CAAI Transactions on Intelligent Systems,2013,8(2):95-104.(in Chinese)
[4] 柴玉梅,张卓,等.基于频繁概念直乘分布的全局闭频繁项集挖掘算法[J].计算机学报,2012,35(5):990-1000. CHAI Yu-mei,ZHANG Zhuo,et al.An algorithm for mining global closed frequent itemsets based on distributed frequent concept direct product[J].Chinese Journal of Computers,2012,35(5):990-1000.(in Chinese)
[5] 孙岳,毛国君,等.基于多分类器的数据流中的概念漂移挖掘[J].自动化学报,2008,34(1):93-97. SUN Yue,MAO Guo-jun,et al.Mining concept drifts from data streams based on multi-classifiers[J].Acta Automatica Sinica,2008,34(1):93-97.(in Chinese)
[6] 欧阳震诤,罗建书,等.一种不平衡数据流集成分类模型[J].电子学报,2010,38(1):184-189. OUYANG Zhen-zheng,LUO Jian-shu,et al.An ensemble classifier frame work for mining imbalanced data streams[J].Acta Electronica Sinica,2010,38(1):184-189.(in Chinese)
[7] ELWELL R,POLIKAR R.Incremental learning of concept drift in non-stationary environments [J].IEEE Transactions on Neural Networks,2011,22(10):1517-1531.
[8] 朱群,张玉红,等.一种基于双层窗口的概念漂移数据流分类算法[J].自动化学报,2011,9(37):1077-1084. ZHU Qun,ZHANG Yu-hong,et al.A double-window-based classification algorithm for concept drifting data streams[J].Acta Automatica Sinica,2011,9(37):1077-1084.(in Chinese)
[9] 徐文华,覃征,常扬.基于半监督学习的数据流集成分类算法[J].模式识别与人工智能,2012,25(2):292-299. XU Wen-hua,QIN Zheng,CHANG Yang.Semi-supervised learning based ensemble classifier for stream data[J].Pattern Recognition and Artificial Intelligence,2012,25(2):292-299.(in Chinese)
[10] PIOTR S,MICHAL W.Concept drift detection and model selection with simulated recurrence and ensembles of statistical detectors[J].Journal of Universal Computer Science,2013,19(4):462-483.
[11] PAULO M G,ROBERTO S M.RCD:A recurring concept drift framework[J].Pattern Recognition Letters,2013,34 (9):1018-1025.
[12] DEWAN M F,LI Z,et al.An adaptive ensemble classifier for mining concept drifting data streams[J].Expert Systems with Applications,2013,40(15):5895-5906.
[13] KLINKENBERG R.Learning drifting concepts:example selection vs.example weighting [J].Intelligent Data Analysis,2004,8(3):281-300.
[14] PETER V,ABRANHAM B.Entropy-based concept drift detection[A].Han J W.Proceedings of the 6th International Conference on Data Mining[C].Houston:IEEE Computer Society,2006.1113-1118.
[15] 于剑,石洪波,等.关于极大熵聚类算法的收敛性定理的反例[J].中国科学E辑:技术科学,2003,33(6):531-536.
[16] BLEI D M,NG A Y,et al.Latent dirichlet allocation [J].Journal of Machine Learning Research,2003,(3):993-1022.
[17] 石晶,范猛,等.基于LDA模型的主题分析[J].自动化学报,2009,35(12):1586-1592. SHI Jing,FAN Meng,et al.Topic analysis based on LDA model[J].Acta Automatica Sinica,2009,35(12):1586-1592.(in Chinese) |