[1] 边肇祺,张学工.模式识别[M].2版.北京:清华大学出版社,2000:176-178.(BIAN Z Q, ZHANG X G. Pattern Recognition[M]. 2nd ed. Beijing:Tsinghua University Press, 2000:176-178.) [2] DASH M, LIU H. Feature selection for classification[J]. Intelligent Data Analysis, 1997, 1(3):131-156. [3] KOHAVI R, JOHN G H. Wrappers for feature subset selection[J]. Artificial Intelligence, 1997, 97(1/2):273-324. [4] 李志杰,李元香,王峰,等.面向大数据分析的在线学习算法综述[J].计算机研究与发展,2015,52(8):1707-1721.(LI Z J, LI Y X, WANG F, et al. Online learning algorithms for big data analytics:a survey[J]. Journal of Computer Research and Development, 2015, 52(8):1707-1721.) [5] WU X, YU K, DING W, et al. Online feature selection with streaming features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5):1178-1192. [6] PERKINS S, THEILER J. Online feature selection using grafting[EB/OL].[2016-01-22]. http://public.lanl.gov/jt/Papers/perkins_icml03.pdf. [7] ZHOU J, FOSTER D, STINE R, et al. Streaming feature selection using alpha-investing[EB/OL].[2016-02-06]. http://www.cis.upenn.edu/~ungar/Datamining/Publications/p384-zhou.pdf. [8] ZHOU J, FOSTER D P, STINE R A, et al. Streamwise feature selection[J]. Journal of Machine Learning Research, 2006, 7:1861-1885. [9] WANG J, ZHAO P, HOI S C H, et al. Online feature selection and its applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3):698-710. [10] NOGUEIRA S, BROWN G. Measuring the stability of feature selection with applications to ensemble methods[EB/OL].[2016-02-03]. http://xueshu.baidu.com/s?wd=paperuri%3A%281a009adab91ad944631001ba336f4e25%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.728.549%26rep%3Drep1%26type%3Dpdf&ie=utf-8&sc_us=5540872035374925413. [11] YU K, WU X, DING W, et al. Towards scalable and accurate online feature selection for big data[C]//Proceedings of the 2014 IEEE International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2014:660-669. [12] 黄莎莎.稳定的特征选择算法研究[D].南京:南京邮电大学,2014.(HUANG S S. Stable feature selection algorithm[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2014.) [13] 黄莎莎.基于特征聚类集成技术的组特征选择方法[J].微型机与应用,2014(11):79-82.(HUANG S S. Group feature selection based on feature clustering ensemble[J]. Microcomputer and its Applications, 2014(11):79-82.) [14] LOSCALZO S, YU L, DING C. Consensus group stable feature selection[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2009:567-576. [15] AU W H, CHAN K C C, WONG A K C, et al. Attribute clustering for grouping, selection, and classification of gene expression data[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2005, 2(2):83-101. [16] YU L, DING C, LOSCALZO S. Stable feature selection via dense feature groups[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2008:803-811. [17] GUO Z, ZHANG T, LI X, et al. Towards precise classification of cancers based on robust gene functional expression profiles[J]. BMC Bioinformatics, 2005, 6(1):1-12. [18] RAPAPORT F, ZINOVYEV A, DUTREIX M, et al. Classification of microarray data using gene networks[J]. BMC Bioinformatics, 2007, 8(1):1-15. [19] KLEINBERG J. An impossibility theorem for clustering[EB/OL].[2016-02-15]. http://www.cc.gatech.edu/~isbell/classes/reading/papers/kleinberg-nips15.pdf. [20] DIETTERICH T G. Ensemble methods in machine learning[M]//Multiple Classifier Systems, LNCS 1857. Berlin:Springer, 2000:1-15. [21] 罗会兰.聚类集成关键技术研究[D].杭州:浙江大学,2007.(LUO H L. Research on key technologies of clustering ensemble[D]. Hangzhou:Zhejiang University, 2007.) [22] FRED A. Finding consistent clusters in data partitions[EB/OL].[2016-02-01]. http://xueshu.baidu.com/s?wd=paperuri%3A%284b1317d334fc32b2cec0dde7e8a4ca2b%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Bjsessionid%3D444EE0EED4CB34E00254EB7CB735820B%3Fdoi%3D10.1.1.97.1296%26rep%3Drep1%26type%3Dpdf&ie=utf-8&sc_us=5845832111985885344. [23] STREHL A, GHOSH J. Cluster ensembles-a knowledge reuse framework for combining multiple partitions[J]. Journal of Machine Learning Research, 2003, 3:583-617. [24] FERN X Z, BRODLEY C. Random projection for high-dimensional data clustering:a cluster ensemble approach[C]//Proceedings of the 20th International Conference on Machine Learning. Menlo Park, CA:AAAI Press, 2003:186-193. [25] GAO J, FAN W, HAN J. On the power of ensemble:supervised and unsupervised methods reconciled-an overview of ensemble methods[C]//Proceedings of the 2010 SIAM International Conference on Data Mining. Columbus, Ohio:SIAM, 2010:2-14. [26] FRED A. Finding consistent clusters in data partitions[M]//Multiple Classifier Systems, LNCS 2096. Berlin:Springer, 2001:309-318. [27] PENA J M. Learning Gaussian graphical models of gene networks with false discovery rate control[C]//Proceedings of the 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Berlin:Springer, 2008:165-176. [28] UCI. Machine learning repository[DB/OL].[2016-01-11]. http://archive.ics.uci.edu/ml/. |