Zliobaite, I., 2011. Combining similarity in time and space for training set formation under concept drift. Intelligent Data Analysis, 15 (4), pp. 589-611.
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Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications.
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
|Group:||School of Design, Engineering & Computing|
|Deposited By:||Dr Indre Zliobaite LEFT|
|Deposited On:||06 Oct 2011 10:03|
|Last Modified:||07 Mar 2013 15:48|
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- Combining Similarity in Time and Space for Training Set Formation under Concept Drift. (deposited 10 Apr 2011 19:19)
- Combining similarity in time and space for training set formation under concept drift. (deposited 06 Oct 2011 10:03) [Currently Displayed]
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