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Online-Offline Extreme Learning Machine with Concept Drift Tracking for Time Series Data
- Source :
- DICTA
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges during learning from data streams is reacting to concept drift, i.e. unforeseen changes of the stream's underlying data distribution. Traditional methods always used online learning to handle the concept drift problem. However, online learning requires high time cost during online training. To overcome this shortcoming, this paper proposes a Kalman filtering approach, which can provide robust concept drift detection, to track concept drift. Once concept drift happens, the online extreme learning machine is applied to update the tracking model, whereas the offline extreme learning machine is used when no concept drift occurs. Based on this idea, we propose a fusion framework to combine online and offline extreme learning machine to efficiently track the data stream. The experiment results indicate the superior performance of our method.
- Subjects :
- Online and offline
Data stream
Concept drift
Computer science
business.industry
Active learning (machine learning)
Data stream mining
Online machine learning
02 engineering and technology
computer.software_genre
Machine learning
Data modeling
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Data mining
business
computer
Extreme learning machine
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
- Accession number :
- edsair.doi...........9a17969b470e96e10da31352286eb1e8