Burst Detection-Based Selective Classifier Resetting

Scott Wares (), John Isaacs () and Eyad Elyan ()
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Scott Wares: Robert Gordon University, Sir Ian Wood Building Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, USA
John Isaacs: Robert Gordon University, Sir Ian Wood Building Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, USA
Eyad Elyan: Robert Gordon University, Sir Ian Wood Building Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, USA

Journal of Information & Knowledge Management (JIKM), 2021, vol. 20, issue 02, 1-14

Abstract: Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-Based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.

Keywords: Data streaming; concept drift; temporal dependence (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219649221500271

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