Year: 2026
Type: article
Abstract: Concept drift detection is an important technique for handling issues in real-time online learning. Learning in a real-time environment is challenging because data distribution is dynamic. This dynamic nature of data distribution affects the performance of the learning algorithm. Therefore, drift detection algorithms are used to handle such issues. In this article, we introduce a simple algorithm, the Shifting Classifier's Mean Error Rate (SCMER), which identifies the drift position using the classifier's mean error rate, computed from a range of instances (e.g., from 1 to n). The algorithm identifies the drift position when the deviation between two cumulative mean error rates is greater than a threshold value, ϵ. We proposed a Hellinger distance-inspired metric for measuring the deviation between two cumulative mean error rates. For hypothesis testing, we adapted the Hoeffding Bound. Experimental analysis shows that our algorithm detects drifts with shorter detection delays, fewer false positives, and fewer false negatives when compared to state-of-the-art methods.
DOI: 10.14419/nse20c73
Authors: Nagaraj Honnikoll, Ishwar Baidari
Institution: Karnatak University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7208739710