Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams

Zhang, Hang; Liu, Weike; Wang, Shuo; Shan, Jicheng; Liu, Qingbao

doi:10.1109/access.2019.2914725

Cited by 29 publications

(7 citation statements)

References 28 publications

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“…In [45] stratified bootstrapping is employed where each data chunk is used to learn a stratified bagging classifier which itself consists of several base estimators learned from a stratum of that data chunk. Another approach, namely RE-DI, is proposed in [46] which uses a long-term classifier to handle gradual concept drifts and multiple dynamic classifiers to handle sudden concept drifts. The classifier weights are influenced by two sources: a time-decaying mechanism used to focus on newer concepts and a reinforcement mechanism to increase the weight of classifiers that perform better on minority classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Nouri,

Kiani,

Fadishei

2024

Statistical Analysis

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Today's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO.

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Section: Literature Reviewmentioning

confidence: 99%

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Nouri,

Kiani,

Fadishei

2024

Statistical Analysis

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“…Weights assigned for each base classifier can be continuously updated to reflect their current competencies on minority classes (Ren et al, 2018). A reinforcement learning mechanism can be used to increase the weights of the base classifiers that perform better on the minority class (Zhang et al, 2019). One can use a hybrid approach that combines resampling minority instances with dynamic weighting base classifiers based on their predictive performance on sliding windows of minority samples (Yan et al, 2022).…”

Section: Ensembles For Imbalanced Data Streamsmentioning

confidence: 99%

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Aguiar¹,

Krawczyk²,

Cano³

2022

Preprint

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Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.

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“…The model may also use a dynamic approach where learners are added and removed dynamically, and rely on the most recent data to make a prediction [15]. Addictive Expert Ensembles (AddExp) [34], Dynamic Weighted Majority (DWM) [33], Geometrically Optimum and Online-Weighted Ensemble (GOOWE) [7], Learn++.NSE [18], Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI) [53] and Dynamic Adaption to Concept Changes (DACC) [12] fall into this category .…”

Section: Related Workmentioning

confidence: 99%

A Broad Ensemble Learning System for Drifting Stream Classification

Bakhshi¹,

Ghahramanian²,

Bonab³

et al. 2021

Preprint

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Data stream classification has become a major research topic due to the increase in temporal data. One of the biggest hurdles of data stream classification is the development of algorithms that deal with evolving data, also known as concept drifts. As data changes over time, static prediction models lose their validity. Adapting to concept drifts provides more robust and better performing models. The Broad Learning System (BLS) is an effective broad neural architecture recently developed for incremental learning. BLS cannot provide instant response since it requires huge data chunks and is unable to handle concept drifts. We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves best-in-class model accuracy. It employs a dynamic output ensemble layer to address the limitations of BLS. We present its mathematical derivation, provide comprehensive experiments with 11 datasets that demonstrate the adaptability of our model, including a comparison of our model with BLS, and provide parameter and robustness analysis on several drifting streams, showing that it statistically significantly outperforms seven state-of-the-art baselines. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.

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Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams

Cited by 29 publications

References 28 publications

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

A Broad Ensemble Learning System for Drifting Stream Classification

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