[1] WANG M, LI H, TAO D C, et al. Multimodal Graph-Based Reranking for Web Image Search. IEEE Transactions on Image Processing, 2012, 21(11): 4649-4661.
[2] WANG J L, ZHAO P L, HOI S C H, et al. Online Feature Selection and Its Applications. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3): 698-710.
[3] ROBNIK-SIKONJA M, KNONNENKO I. Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 2003, 53(1/2): 23-69.
[4] HOI S C H, WANG J L, ZHAO P L, et al. Online Feature Selection for Mining Big Data // Proc of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications. New York, USA: ACM, 2012: 93-100.
[5] LI K W, YU M X, LIU L, et al. Feature Selection Method Based on Weighted Mutual Information for Imbalanced Data. International Journal of Software Engineering and Knowledge Engineering, 2018, 28(8): 1177-1194.
[6] DING W, STEPINSKI T F, MU Y, et al. Subkilometer Crater Discovery with Boosting and Transfer Learning. ACM Transactions on Intelligent Systems and Technology, 2011, 2(4). DOI: 10.1145/1989734.1989743.
[7] 王晨曦,林耀进,唐 莉,等.基于信息粒化的多标记特征选择算法.模式识别与人工智能, 2018, 31(2): 123-131.
(WANG C X, LIN Y J, TAN L, et al. Multi-label Feature Selection Based on Information Granulation. Pattern Recognition and Artificial Intelligence, 2018, 31(2): 123-131.)
[8] WU G, CHANG E Y. Class-Boundary Alignment for Imbalanced Dataset Learning // Proc of the Workshop on Learning from Imba-lanced Datasets. New York, USA: ACM, 2003: 49-56.
[9] 刘景华,林梦雷,王晨曦,等.基于局部子空间的多标记特征选择算法.模式识别与人工智能, 2016, 29(3): 240-251.
(LIN J H, LIN M L, WANG C X, et al. Multi-label Feature Selection Algorithm Based on Local Subspace. Pattern Recognition and Artificial Intelligence, 2016, 29(3): 240-251.)
[10] LIANG J Y, WANG F, DANG C Y, et al. An Efficient Rough Feature Selection Algorithm with a Multi-granulation View. International Journal of Approximate Reasoning, 2012, 53(6): 912-926.
[11] GU Q Q, LI Z H, HAN J W. Generalized Fisher Score for Feature Selection // Proc of the 27th Conference on Uncertainty in Artificial Intelligence. Berlin, Germany: Springer, 2011: 266-273.
[12] PENG H C, LONG F H, DING C. Feature Selection Based on Mutual Information: Criteria of Max-Dependence, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238.
[13] TIBSHIRANI R. Regression Shrinkage and Selection via the Lasso. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 58(1): 267-288.
[14] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357.
[15] GUYON I, ELISSEEFF A. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 2003, 3: 1157-1182.
[16] LI H G, WU X D, LI Z, et al. Group Feature Selection with Streaming Features // Proc of the 13th IEEE International Confe-rence on Data Mining. Washington, USA: IEEE, 2013: 1109-1114.
[17] WANG J, WANG M, LI P P, et al. Online Feature Selection with Group Structure Analysis. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(11): 3029-3041.
[18] ZHOU J, FOSTER D P, STINE R A, et al. Streamwise Feature Selection. Journal of Machine Learning Research, 2006, 3: 1861-1885.
[19] ZHOU P, HU X G, LI P P, et al. Online Feature Selection for High-Dimensional Class-Imbalanced Data. Knowledge-Based Systems, 2017, 136: 187-199.
[20] WU X D, YU K, DING W, et al. Online Feature Selection with Streaming Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5): 1178-1192.
[21] YU K, WU X D, DING W, et al. Scalable and Accurate Online Feature Selection for Big Data. ACM Transactions on Knowledge Discovery from Data, 2016, 11(2). DOI: 10.1145/2976744.
[22] LIU J H, LIN Y J, LI Y W, et al. Online Multi-label Streaming Feature Selection Based on Neighborhood Rough Set. Pattern Re-cognition, 2018, 84: 273-287.
[23] LIN Y J, HU Q H, ZHANG J, et al. Multi-label Feature Selection with Streaming Labels. Information Sciences, 2016, 372: 256-275.
[24] 胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简.软件学报, 2008, 19(3): 640-649.
(HU Q H, YU D R, XIE Z X. Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation. Journal of Software, 2008, 19(3): 640-649.) |