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

Article type: Research Article

Authors: Žliobaitė, Indrė^{a; b; *}

Affiliations: [a] Vilnius University, Vilnius, Lithuania | [b] Eindhoven University of Technology, Eindhoven, The Netherlands

Correspondence: [*] Address for correspondence: Eindhoven University of Technology, PO Box 513, NL 5600 MB Eindhoven, The Netherlands. Tel.: +31 40 247 2733; Fax: +31 40 246 3992; E-mail: zliobaite@gmail.com.

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.

Keywords: Concept drift, gradual drift, online learning, instance selection

DOI: 10.3233/IDA-2011-0484

Journal: Intelligent Data Analysis, vol. 15, no. 4, pp. 589-611, 2011