Book Chapter10.1007/978-3-540-75488-6_27
Detecting concept drift using statistical testing
Kyosuke Nishida,Koichiro Yamauchi +1 more
- 01 Oct 2007
- pp 264-269
291
TL;DR: This work has developed a detection method that uses a statistical test of equal proportions to detect concept drift in five synthetic datasets that contained various types of concept drift.
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Abstract: Detecting concept drift is important for dealing with realworld online learning problems. To detect concept drift in a small number of examples, methods that have an online classifier and monitor its prediction errors during the learning have been developed. We have developed such a detection method that uses a statistical test of equal proportions. Experimental results showed that our method performed well in detecting the concept drift in five synthetic datasets that contained various types of concept drift.
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Citations
A survey on concept drift adaptation
TL;DR: The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Ensemble learning for data stream analysis
TL;DR: This paper surveys research on ensembles for data stream classification as well as regression tasks and discusses advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs.
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Learning under Concept Drift: A Review
TL;DR: A high quality, instructive review of current research developments and trends in the concept drift field is conducted, and a framework of learning under concept drift is established including three main components: concept drift detection, concept drift understanding, and concept drift adaptation.
995
Learning in Nonstationary Environments: A Survey
TL;DR: In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.
Learning under Concept Drift: A Review
TL;DR: In this paper, the authors present a review of the recent research in the field of concept drift and propose a framework of learning under concept drift. But, the focus of this survey is on the detection, understanding and adaptation of the concept drift in streaming data.
752
References
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Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten,Eibe Frank,Mark Hall +2 more
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TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Data mining
TL;DR: The graduate certificate’s narrow focus allows you to dig deep into this specific topic, and start applying your knowledge sooner.
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Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TL;DR: A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.
Novelty detection: a review—part 1: statistical approaches
M. Markou,Sameer Singh +1 more
TL;DR: There are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.
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Learning with Drift Detection
João Gama,Pedro Medas,Gladys Castillo,Gladys Castillo,Pedro Pereira Rodrigues +4 more
- 29 Sep 2004
TL;DR: A method for detection of changes in the probability distribution of examples, to control the online error-rate of the algorithm and to observe that the method is independent of the learning algorithm.
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