[1] Pawlak Z.Rough Sets-Theoretical Aspect of Reasoning about Data[M].Dordrecht:Kluwer Academic Publishers,1991.
[2] 邓大勇,陈林.并行约简与F-粗糙集.云模型与粒计算[M].北京:科学出版社,2012:210-228. Deng D Y,Chen L.Parallel Reducts and F-rough Sets.Cloud Model and Granular Computing[M].Beijing:Science Press,2012:210-228.(in Chinese)
[3] Qian J,Miao D Q,Zhang Z H,et al.Parallel reduction algorithm using MapReduce[J].Information Sciences,2014,279:671-690.
[4] Wang F,Xu J,Li L.A novel rough set reduct algorithm to feature selection based on artificial fish swarm algorithm[A].LNCS8795:Proc of 5th International Conference on Swarm Intelligence[C].Berlin:Springer,2014.24-33.
[5] Liu Y,Huang W L,Jiang Y L,et al.Quick attribute reduct algorithm for neighborhood rough set model[J].Information Sciences,2014,271:65-81.
[6] Hu F,Wang G Y.Knowledge reduction based on divide and conquer method in rough set theory[J].Mathematical Problems in Engineering,2012(1):542-551.
[7] Eskandari S,Javidi M M.Online streaming feature selection using rough sets[J].International Journal of Approximate Reasoning,2016,69(C):35-57.
[8] Lin T Y,Liu Y,Huang W L.Unifying rough set theories via large scaled granular computing[J].Fundamenta Informaticae,2013,127:413-428.
[9] Cao F Y,Huang J Z.A concept-drfting detection algorithm for categorical evolving data[A].LNAI 7819:Proc of the 17th Pacific-Asia Conf on Knowledge Discovery and Data Mining[C].Berlin:Springer,2013.485-496.
[10] 邓大勇,徐小玉,黄厚宽.基于并行约简的概念漂移探测[J].计算机研究与发展,2015,52(5):1071-1079. Deng D Y,Xu X Y,Huang H K.Concept drifting detection for categorical evolving data based on parallel reducts[J].Journal of Computer Research and Development,2015,52(5):1071-1079.(in Chinese)
[11] 邓大勇,苗夺谦,黄厚宽.信息表中概念漂移与不确定性分析[J].计算研究与发展,2016,53(11):2607-2612. Deng D Y,Miao D Q,Huang H K.Analysis of concept drifting and uncertainty in an information system[J].Journal of Computer Research and Development,2016,53(11):2607-2612.(in Chinese)
[12] 梁吉业,钱宇华,李德玉,等.大数据挖掘的粒计算理论与方法[J].中国科学E辑信息科学,2015,45(11):1355-1369. Liang J Y,Qian Y H,Li D Y,et al.Theory and method of granular computing for big data mining[J].Science in China Ser E Information Sciences,2015,45(11):1355-1369.(in Chinese)
[13] Hu Q,Yu D,Liu J,et al.Neighborhood rough set based heterogeneous feature subset selection[J].Information Sciences,2008,178(18):3577-3594.
[14] Chen D,Yang Y.Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models[J].IEEE Transactions on Fuzzy Systems,2014,22(5):1325-1334.
[15] Qian Y,Liang J,Yao Y,et al.MGRS:A multi-granulation rough set[J].Information Sciences,2010,180(6):949-970.
[16] Lu N,Zhang G,Lu J.Concept drift detection via competence models[J].Artificial Intelligence,2014,209(1):11-28.
[17] Lu N,Lu J,Zhang G.A concept drift-tolerant case-base editing technique[J].Artificial Intelligence,2015,230(C):108-133.
[18] 孙雪,李昆仑,韩蕾,等.基于特征项分布的信息熵及特征动态加权概念漂移检测模型[J].电子学报,2015,43(7):1356-1361. Sun X,Li K L,Han L,et al.Construction of the concept drift detection model based on the information entropy of feature distribution and dynamic weighting algorithm[J].Acta Electronica Sinica,2015,43(7):1356-1361.(in Chinese)
[19] Li P,Wu X,Hu X,Learning concept-drifting data streams with random ensemble decision trees[J].Neurocomputing,2015,166(C):68-83. |