International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 117 - Number 13 |
Year of Publication: 2015 |
Authors: Veena Mittal, Indu Kashyap |
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Veena Mittal, Indu Kashyap . Online Methods of Learning in Occurrence of Concept Drift. International Journal of Computer Applications. 117, 13 ( May 2015), 18-22. DOI=10.5120/20614-3280
Due to potentially large number of applications of real-time data stream mining in scientific and business analysis, the real-time data streams mining has drawn attention of many researchers who are working in the area of machine learning and data mining. In many cases, for real-time data stream mining online learning is used. Environments that require online learning are non-stationary and whose underlying distributions may change over time i. e. concept drift, because of which mining of real- time data streams with concept drifts is quite challenging. However, ensemble methods have been suggested for this particular situation. This paper reviews various online methods of drift detection. We also present some results of our experiments that show the comparison of some online drift detection (concept drift) methods.