Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/56795
Title: Concept drift detection with quadtree-based spatial mapping of streaming data
Keywords: Data stream
Concept drift
Drift detector
Online learning
Issue Date: May-2023
Publisher: Elsevier
Citation: COELHO, R. A.; TORRES, L. C. B.; CASTRO, C. L. de. Concept drift detection with quadtree-based spatial mapping of streaming data. Information Sciences, [S.l.], v. 625, p. 578-592, May 2023.
Abstract: Online learning is a complex task, especially when the data stream changes its distribution over time. It’s challenging to monitor and detect these changes to maintain the performance of the learning algorithm. In this work, we present a novel detection method built from a different perspective of other preexisting detectors from literature. It analyzes the space occupied by the data, assuming that it would be immutable unless changes in this space occur among data of different classes. The data is mapped into a quadtree-based memory structure that provides knowledge about which class (label) is dominant in a given region of the feature space. Drifts are detected by checking whether data assigned to a given class occupy spaces considered relevant to the other class. The proposed method was evaluated on benchmark binary classification problems. The results show that our method can compete with well-known drift detectors from the literature on synthetic and real-world datasets.
URI: https://www.sciencedirect.com/science/article/pii/S0020025522015808
http://repositorio.ufla.br/jspui/handle/1/56795
Appears in Collections:DCC - Artigos publicados em periódicos

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