Combining similarity in time and space for training set formation under concept drift.

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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DOI: 10.3233/IDA-2011-0484

Abstract

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.

Item Type:Article
ISSN:1088-467X
Subjects:Generalities > Computer Science and Informatics > Artificial Intelligence
Group:School of Design, Engineering & Computing > Smart Technology Research Centre
ID Code:18567
Deposited By:Dr Indre Zliobaite LEFT
Deposited On:06 Oct 2011 10:03
Last Modified:07 Mar 2013 15:48

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