Online-Offline Extreme Learning Machine with Concept Drift Tracking for Time Series Data

2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016

Volumn , Issue , 2016, Pages

  Guo, Lihua ^a   Liew, Alan Wee Chung ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b GRIFFITH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA COMMUNICATION SYSTEMS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; SENSOR NETWORKS;

DATA DISTRIBUTION; DATA STREAM MINING; EXTREME LEARNING MACHINE; KALMAN-FILTERING; ONLINE LEARNING; ONLINE TRAINING; TIME-SERIES DATA; TRACKING MODELS;

E-LEARNING;

EID: 85011110660     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DICTA.2016.7797069     Document Type: Conference Paper

References (19)

1. http://www.idc.com/getdoc.jsp?containerId=prUS40560115
2. Gama, J., et al. "A survey on concept drift adaptation." ACM Computing Surveys (CSUR) 46.4 (2014): 44
3. Fdez-Riverola, F., et al. "Applying lazy learning algorithms to tackle concept drift in spam filtering." Expert Systems with Applications 33.1 (2007): 36-48
4. Widmer, G., and Miroslav K. "Learning in the presence of concept drift and hidden contexts." Machine learning 23.1 (1996): 69-101
5. Tsai, Cheng-Jung, Chien-I. Lee, and Wei-Pang Yang. "Mining decision rules on data streams in the presence of concept drifts." Expert Systems with Applications 36.2 (2009): 1164-1178
6. Zliobaite, Indre, et al. "Active learning with drifting streaming data." IEEE transactions on neural networks and learning systems 25.1 (2014): 27-39
7. Bose RP, Jagadeesh Chandra, et al. "Dealing with concept drifts in process mining." IEEE transactions on neural networks and learning systems 25.1 (2014): 154-171
8. Brzezinski, Dariusz, and Jerzy Stefanowski. "Reacting to different types of concept drift: The accuracy updated ensemble algorithm." IEEE Transactions on Neural Networks and Learning Systems 25.1 (2014): 81-94
9. Canzian, Luca, Yu Zhang, and Mihaela Schaar. "Ensemble of distributed learners for online classification of dynamic data streams." IEEE Transactions on Signal and Information Processing over Networks 1.3 (2015): 180-194
10. Pan, Shirui, et al. "Graph ensemble boosting for imbalanced noisy graph stream classification." IEEE transactions on cybernetics 45.5 (2015): 954-968
11. Street, W. Nick, and YongSeog Kim. "A streaming ensemble algorithm (SEA) for large-scale classification." Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001
12. Kolter, J. Zico, and Marcus A. Maloof. "Dynamic weighted majority: An ensemble method for drifting concepts." Journal of Machine Learning Research 8.Dec (2007): 2755-2790
13. Bifet, Albert, and Ricard Gavalda. "Learning from Time-Changing Data with Adaptive Windowing." SDM. Vol. 7. 2007
14. Dietterich, Thomas G. "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization." Machine learning 40.2 (2000): 139-157
15. Elwell, Ryan, and Robi Polikar. "Incremental learning of concept drift in nonstationary environments." IEEE Transactions on Neural Networks 22.10 (2011): 1517-1531
16. Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. "Extreme learning machine theory and applications." Neurocomputing 70.1 (2006): 489-501
17. Huang, Guang-Bin, and Lei Chen. "Convex incremental extreme learning machine." Neurocomputing 70.16 (2007): 3056-3062
18. Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of basic Engineering, 1960, 82(1): 35-45
19. U.S. National Oceanic and Atmospheric Administration. Federal Climate Complex Global Surface Summary of Day Data [Online]. Available: ftp.ncdc.noaa.gov/pub/data/gsod