A Pruning Optimized Fast Learn++NSE Algorithm

Chen, Yong; Zhu, Yuquan; Chen, Haifeng; Shen, Yan; Zhao, Xue

doi:10.1109/access.2021.3118568

Cited by 3 publications

(2 citation statements)

References 30 publications

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Section: Experimental Methodologymentioning

confidence: 99%

“…FASE, LevBag, OBag, OBoost, DWM and Learn++.NSE use HT as the base learner, whereas ARF uses its built in ARFHoeffdingTree as the base learner. As the ensemble time of Learn++.NSE increases exponentially with the number of base classifiers (Chen et al, 2021), we use its pruning enabled version within MOA for execution efficiency. Heterogeneous ensembles : We include BLAST, as it is a SOTA technique based on online algorithm selection (Luong et al, 2021). Fading factor based variant of BLAST is used, due to its merits reported by the authors (van Rijn et al, 2018).…”

Section: Experimental Methodologymentioning

confidence: 99%

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Multi‐armed bandit based online model selection for concept‐drift adaptation

Wilson,

Chaudhury,

Lall

2024

Expert Systems

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Ensemble methods are among the most effective concept‐drift adaptation techniques due to their high learning performance and flexibility. However, they are computationally expensive and pose a challenge in applications involving high‐speed data streams. In this paper, we present a computationally efficient heterogeneous classifier ensemble entitled OMS‐MAB which uses online model selection for concept‐drift adaptation by posing it as a non‐stationary multi‐armed bandit (MAB) problem. We use a MAB to select a single adaptive learner within the ensemble for learning and prediction while systematically exploring promising alternatives. Each ensemble member is made drift resistant using explicit drift detection and is represented as an arm of the MAB. An exploration factor controls the trade‐off between predictive performance and computational resource requirements, eliminating the need to continuously train and evaluate all the ensemble members. A rigorous evaluation on 20 benchmark datasets and 9 algorithms indicates that the accuracy of OMS‐MAB is statistically at par with state‐of‐the‐art (SOTA) ensembles. Moreover, it offers a significant reduction in execution time and model size in comparison to several SOTA ensemble methods, making it a promising ensemble for resource constrained stream‐mining problems.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Multi‐armed bandit based online model selection for concept‐drift adaptation

Wilson,

Chaudhury,

Lall

2024

Expert Systems

View full text Add to dashboard Cite

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A multiple classifiers time-serial ensemble pruning algorithm based on the mechanism of forward supplement

et al. 2022

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A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer

et al. 2023

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Data stream mining deals with processing large amounts of data in nonstationary environments, where the relationship between the data and the labels often changes. Such dynamic relationships make it difficult to design a computationally efficient data stream processing algorithm that is also adaptable to the nonstationarity of the environment. To make the algorithm adaptable to the nonstationarity of the environment, concept drift detectors are attached to detect the changes in the environment by monitoring the error rates and adapting to the environment’s current state. Unfortunately, current approaches to adapt to environmental changes assume that the data stream is fully labeled. Assuming a fully labeled data stream is a flawed assumption as the labeling effort would be too impractical due to the rapid arrival and volume of the data. To address this issue, this study proposes to detect concept drift by anticipating a possible change in the true label in the high confidence prediction region. This study also proposes an ensemble-based concept drift adaptation approach that transfers reliable classifiers to the new concept. The significance of our proposed approach compared to the current baselines is that our approach does not use a performance measur as the drift signal or assume a change in data distribution when concept drift occurs. As a result, our proposed approach can detect concept drift when labeled data are scarce, even when the data distribution remains static. Based on the results, this proposed approach can detect concept drifts and fully supervised data stream mining approaches and performs well on mixed-severity concept drift datasets.

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A Pruning Optimized Fast Learn++NSE Algorithm

Cited by 3 publications

References 30 publications

Multi‐armed bandit based online model selection for concept‐drift adaptation

Multi‐armed bandit based online model selection for concept‐drift adaptation

A multiple classifiers time-serial ensemble pruning algorithm based on the mechanism of forward supplement

A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer

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