Concept Drift Detection with Hierarchical Hypothesis Testing

Yu, Shujian; Abraham, Zubin

doi:10.1137/1.9781611974973.86

Cited by 47 publications

(16 citation statements)

References 13 publications

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“…HLFR: This method by [22] uses Hierarchical Linear Four Rates. HLFR runs using two layers: the responsibility of detecting potential drifts belongs to the first layer, whereas the second layer validates the detected drift and communicates this information back to the first layer.…”

Section: Existing Concept Drift Detection Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Existing Concept Drift Detection Methodsmentioning

confidence: 99%

“…Then, lines (22)(23)(24) incrementally train the current model and keep it up-to-date with the newly labelled data (N ld ). Lines (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39) check and handle the availability of each class, phase five (class is missing?).…”

Section: The Dmddm-s Algorithmmentioning

confidence: 99%

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

et al. 2020

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“…Adaptation methods are to relearn the models or to use ensemble algorithms to adapt to new concepts [3,33,35]. In recent years, drift point detection has been developed to cover more complicated cases, such as feature selection drift [4,39], region selection drift [19,20,25] and the detection of multi-layer drift [1,40]. These developments address the Where criterion.…”

Section: Learning With Concept Driftmentioning

confidence: 99%

Data-driven decision support under concept drift in streamed big data

Song

Zhang

2019

Complex Intell. Syst.

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Data-driven decision-making (D 3 M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data-a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for D 3 M. The study first establishes a technical framework for real-time D 3 M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting D 3 M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of D 3 M in streamed big data environments.

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“…The presented work intends to bring new perspectives to the field of concept drift detection and adaptation with the recent advances in hierarchical mechanism (e.g., [20], [21]) and provides the following contributions. First, we present Hierarchical Linear Four Rates (HLFR) detector [22], a novel HHT-based concept drift detection method, which is applicable to different types of concept drifts (e.g., recurrent or irregular, gradual or abrupt). A detailed analysis on the Type-I and Type-II errors of the proposed HLFR is also performed.…”

Section: Introductionmentioning

confidence: 99%

Concept drift detection and adaptation with hierarchical hypothesis testing

Abraham

Wang³

et al. 2019

Journal of the Franklin Institute

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A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a phenomenon also known as concept drifts), such that their classification performance deteriorates dramatically. In this paper, we first present a hierarchical hypothesis testing (HHT) framework that can detect and also adapt to various concept drift types (e.g., recurrent or irregular, gradual or abrupt), even in the presence of imbalanced data labels. A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR), is implemented under the HHT framework thereafter. By substituting a widely-acknowledged retraining scheme with an adaptive training strategy, we further demonstrate that the concept drift adaptation capability of HLFR can be significantly boosted. The theoretical analysis on the Type-I and Type-II errors of HLFR is also performed. Experiments on both simulated and real-world datasets illustrate that our methods outperform state-of-the-art methods in terms of detection precision, detection delay as well as the adaptability across different concept drift types.

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Concept Drift Detection with Hierarchical Hypothesis Testing

Cited by 47 publications

References 13 publications

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

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

Data-driven decision support under concept drift in streamed big data

Concept drift detection and adaptation with hierarchical hypothesis testing

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