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Open AccessArticle

Fast Reaction to Sudden Concept Drift in the Absence of Class Labels

1
Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3086, Australia
2
Faculty of Information Technology, Monash University, Clayton 3800, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 606; https://doi.org/10.3390/app10020606
Received: 12 December 2019 / Revised: 12 December 2019 / Accepted: 8 January 2020 / Published: 14 January 2020
(This article belongs to the Section Computing and Artificial Intelligence)
A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. One of the serious and challenging problems that appears in a data stream is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Most existing works make an optimistic assumption that all incoming data are labelled and the class labels are available immediately. However, such an assumption is not always valid. Therefore, a lack of class labels aggravates the problem of concept drift detection. With this motivation, we propose a drift detector that reacts naturally to sudden drifts in the absence of class labels. In a novel way, the proposed detector reacts to concept drift in the absence of class labels, where the true label of an example is not necessary. Instead of monitoring the error estimates, the proposed detector monitors the diversity of a pair of classifiers, where the true label of an example is not necessary to determine whether components disagree. Using several datasets, an experimental evaluation and comparison is conducted against several existing detectors. The experiment results show that the proposed detector can detect drifts with less delay, runtime and memory usage.
Keywords: concept drift; data stream mining; semisupervised environment concept drift; data stream mining; semisupervised environment
MDPI and ACS Style

Mahdi, O.A.; Pardede, E.; Ali, N.; Cao, J. Fast Reaction to Sudden Concept Drift in the Absence of Class Labels. Appl. Sci. 2020, 10, 606.

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