Computer Science ›› 2022, Vol. 49 ›› Issue (3): 92-98.doi: 10.11896/jsjkx.210200047
Special Issue: Big Data & Data Scinece
• Database & Big Data & Data Science • Previous Articles Next Articles
XIA Yuan1, ZHAO Yun-long1,2, FAN Qi-lin1
CLC Number:
[1]KRAWCZYK B,MINKU L L,GAMA J,et al.Ensemble lear-ning for data stream analysis:A survey[J].Information Fusion,2017,37:132-156. [2]KHAMASSI I,SAYED-MOUCHAWEH M,HAMMAMI M,et al.Discussion and review on evolving data streams and concept drift adapting[J].Evolving Systems,2018,9(1):1-23. [3]STREET W N,KIM Y S.A streaming ensemble algorithm(SEA) for large-scale classification[C]//Proc. of the Acm Sigkdd Int. Conference on Knowledge Discovery & Data Mining.2001:377-382. [4]WANG H,FAN W,YU P S,et al.Mining concept-drifting data streams using ensemble classifiers[C]//Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2003:226-235. [5]BRZEZINSKI D,STEFANOWSKI J.Reacting to different types of concept drift:The accuracy updated ensemble algorithm[J].IEEE Transactions on Neural Networks and Learning Systems,2013,25(1):81-94. [6]ELWELL R,POLIKAR R.Incremental learning of concept drift in nonstationary environments[J].IEEE Transactions on Neural Networks,2011,22(10):1517-1531. [7]LV Y,PENG S,YUAN Y,et al.A classifier using online bagging ensemble method for bigdata stream learning[J].Tsinghua Science and Technology,2019,24(4):379-388. [8]KOLTER J Z,MALOOF M A.Dynamic weighted majority:An ensemble method for drifting concepts[J].Journal of Machine Learning Research,2007,8(12):2755-2790. [9]PESARANGHADER A,VIKTOR H,PAQUET E.Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams[J].Machine Learning,2018,107(11):1711-1743. [10]OLORUNNIMBE M K,VIKTOR H L,PAQUET E.Dynamic adaptation of online ensembles for drifting data streams[J].Journal of Intelligent Information Systems,2018,50(2):291-313. [11]REN S,LIAO B,ZHU W,et al.Knowledge-maximized ensemblealgorithm for different types of concept drift[J].Information Sciences,2018,430:261-281. [12]CANO A,KRAWCZYK B.Kappa Updated Ensemble for drifting data stream mining[J].Machine Learning,2020,109(1):175-218. [13]RAMÍREZ-GALLEGO S,KRAWCZYK B,GARCÍA S,et al.A survey on data preprocessing for data stream mining:Current status and future directions[J].Neurocomputing,2017,239:39-57. [14]LOSING V,HAMMER B,WERSING H.KNN classifier with self adjusting memory for heterogeneous concept drift[C]//2016 IEEE 16th International Conference on Data Mining (ICDM).IEEE,2016:291-300. [15]ZHOU Z H.Machine learning[M].Beijing:Tsinghua University Press,2016:211-214. [16]SHANNON C E.A mathematical theory of communication[J].ACM SIGMOBILE Mobile Computing and Communications Review,2001,5(1):3-55. [17]BIFET A,HOLMES G,PFAHRINGER B,et al.Moa:Massive online analysis,a framework for stream classification and clustering[C]//Proceedings of the First Workshop on Applications of Pattern Analysis.PMLR,2010:44-50. [18]DOMINGOS P,HULTEN G.Mining high-speed data streams[C]//Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2000:71-80. [19]AGRAWAL R,IMIELINSKI T,SWAMI A.Database mining:A performance perspective[J].IEEE Transactions on Knowledge and Data Engineering,1993,5(6):914-925. [20]LANGLEY P,IBA W,THOMPSON K.An analysis of Bayesian classifiers[C]//AAAI.1992:223-228. [21]OZA N C,RUSSELL S.Experimental comparisons of online and batch versions of bagging and boosting[C]//Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2001:359-364. |
[1] | CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32. |
[2] | ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138. |
[3] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[4] | WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25. |
[5] | LI Xia, MA Qian, BAI Mei, WANG Xi-te, LI Guan-yu, NING Bo. RIIM:Real-Time Imputation Based on Individual Models [J]. Computer Science, 2022, 49(8): 56-63. |
[6] | TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216. |
[7] | YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236. |
[8] | CHEN Yuan-yuan, WANG Zhi-hai. Concept Drift Detection Method for Multidimensional Data Stream Based on Clustering Partition [J]. Computer Science, 2022, 49(7): 25-30. |
[9] | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49. |
[10] | YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63. |
[11] | ZHANG Hong-bo, DONG Li-jia, PAN Yu-biao, HSIAO Tsung-chih, ZHANG Hui-zhen, DU Ji-xiang. Survey on Action Quality Assessment Methods in Video Understanding [J]. Computer Science, 2022, 49(7): 79-88. |
[12] | DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65. |
[13] | LI Xiao-wei, SHU Hui, GUANG Yan, ZHAI Yi, YANG Zi-ji. Survey of the Application of Natural Language Processing for Resume Analysis [J]. Computer Science, 2022, 49(6A): 66-73. |
[14] | DENG Kai, YANG Pin, LI Yi-zhou, YANG Xing, ZENG Fan-rui, ZHANG Zhen-yu. Fast and Transmissible Domain Knowledge Graph Construction Method [J]. Computer Science, 2022, 49(6A): 100-108. |
[15] | HUANG Shao-bin, SUN Xue-wei, LI Rong-sheng. Relation Classification Method Based on Cross-sentence Contextual Information for Neural Network [J]. Computer Science, 2022, 49(6A): 119-124. |
|