Accumulating regional density dissimilarity for concept drift detection in data streams

Liu, Anjin; Liu, Feng; Zhang, Guangquan

doi:10.1016/j.patcog.2017.11.009

Cited by 99 publications

(42 citation statements)

References 50 publications

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“…NN-DVI: This method proposed by the authors of [21] uses a regional-density estimation as a drift detection method, named nearest neighbour-based density variation identification (NN-DVI). This method consists of three components: (i) k-nearest neighbour-based space-partitioning schema (NNPS), which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation; (ii) the density discrepancies are accumulated by a distance function in these subspaces and quantifies the overall differences; (iii) the statistical test defines the confidence interval to detect drift.…”

Section: Existing Concept Drift Detection Methodsmentioning

confidence: 99%

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%

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

et al. 2020

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“…The 0 − 1 encoding scheme can applied to obtain multi-output target matrix C ∈ ℜ T ×m where m is the number of target. This issue limits the feasibility of cross-validation or direct train-test partition methods as an evaluation protocol because those methods assume that the overall data batches are fully observable and risks on loss of data temporal order [14,13].…”

Section: Problem Formulationmentioning

confidence: 99%

Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

Ashfahani¹,

Pratama²

2019

Proceedings of the 2019 SIAM International Conference on Data Mining

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The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. Maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network structures.

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“…The whole concept drift detection process has evolved over time by making such choices and enhancing the process incrementally. Researchers have proposed many concept drift detection methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] in recent times, which have handled this problem in different ways for different situations.…”

Section: Concept Drift Detectionmentioning

confidence: 99%

Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

Toor

Usman

Younas

et al. 2020

Sensors

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With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.

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Accumulating regional density dissimilarity for concept drift detection in data streams

Cited by 99 publications

References 50 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

Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

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