Zliobaite, I. and Kuncheva, L., 2009. Determining the Training Window for Small Sample Size Classification with Concept Drift. In: ICDM Workshops 2009. IEEE International Conference on Data Mining., 6 December 2009, Miami, Fl.,USA, pp. 447-452.
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Official URL: http://www.computer.org/portal/web/csdl/doi/10.110...
We consider classification of sequential data in the presence of frequent and abrupt concept changes. The current practice is to use the data after the change to train a new classifier. However, if the window with the new data is too small, the classifier will be undertrained and hence less accurate that the "old'' classifier. Here we propose a method (called WR*) for resizing the training window after detecting a concept change. Experiments with synthetic and real data demonstrate the advantages of WR* over other window resizing methods.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Dr Indre Zliobaite LEFT|
|Deposited On:||25 Oct 2011 14:25|
|Last Modified:||07 Mar 2013 15:49|
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