Abstract
Dealing with evolving data requires strategies for detecting and quantifying change, and forgetting irrelevant examples during the model revision process. To design an adaptive classifier that is suitable for different types of streams requires us to understand the characteristics of the data stream. Current adaptive classifiers have built-in concept drift detectors used as an estimator at each node. Our research aim is to investigate the usage of different drift detectors for Hoeffding Adaptive Tree (HAT), an adaptive classifier. We proposed three variants of the proposed classifier, called HAT\(_{SEED}\), HAT\(_{HDDM_A}\), and HAT\(_{PHT}\).
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Stirling, M., Koh, Y.S., Fournier-Viger, P., Ravana, S.D. (2018). Concept Drift Detector Selection for Hoeffding Adaptive Trees. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_65
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DOI: https://doi.org/10.1007/978-3-030-03991-2_65
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