Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

Article type: Research Article

Authors: Hu, Hanqing^* | Kantardzic, Mehmed

Affiliations: CECS Department, University of Louisville, KY, USA

Correspondence: [*] Corresponding author: Hanqing Hu, CECS Department, University of Louisville, KY, USA. Tel.: +1 502 852 3626; E-mail: h0hu0004@louisville.edu.

Abstract: Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

Keywords: Concept drift, ensemble, data stream mining

DOI: 10.3233/IDT-210115

Journal: Intelligent Decision Technologies, vol. 15, no. 4, pp. 609-622, 2021