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Adaptive online extreme learning machine by regulating forgetting factor by concept drift map
- Source :
- Neurocomputing. 343:141-153
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- In online-learning, the data is incrementally received and the distributions from which it is drawn may keep changing over time. This phenomenon is widely known as concept drift. Such changes may affect the generalization of a learned model to future data. This problem may be exacerbated by the form of the drift itself changing over time. Quantitative measures to describe and analyze the concept drift have been proposed in previous work. A description composed from these measures is called a concept drift map. We believe that these maps could be useful for guiding how much knowledge in the old model should be forgotten. Therefore, this paper presents an adaptive online learning model that uses a concept drift map to regulate the forgetting factor of an extreme learning machine. Specifically, when a batch of new instances are labeled, the distribution of each class on each attribute is firstly estimated, and then it is compared with the distribution estimated in the previous batch to calculate the magnitude of concept drift, which is further used to regulate the forgetting factor and to update the learning model. Therefore, the novelty of this paper lies in that a quantitative distance metric between two distributions constructed on continuous attribute space is presented to construct concept drift map which can be further associated with the forgetting factor to make the learning model adapt the concept drift. Experimental results on several benchmark stream data sets show the proposed model is generally superior to several previous algorithms when classifying a variety of data streams subject to drift, indicating its effectiveness and feasibility.
- Subjects :
- 0209 industrial biotechnology
Class (computer programming)
Concept drift
Computer science
business.industry
Data stream mining
Generalization
Cognitive Neuroscience
Novelty
02 engineering and technology
Construct (python library)
Space (commercial competition)
Machine learning
computer.software_genre
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
Metric (mathematics)
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Extreme learning machine
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 343
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........199d723c6b288d17810685d340657546