Dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream

Han, Meng; Zhang, Xilong; Chen, Zhiqiang; Wu, Huarui; Li, Muhang

doi:10.1007/s10115-022-01791-5

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…Currently, according to the speed of drift change, it can be classified as sudden drift, gradual drift, incremental drift, and recurrent drift, as shown in Figure 1. The current concept drift data stream learning methods can be divided into active detection methods and passive adaptive methods [20]. Active detection methods usually use a window mechanism to deal with concept drift.…”

Section: Concept Drift Data Streams Learning Methodsmentioning

confidence: 99%

“…The Online Accuracy Updated Ensemble (OAUE) [28] uses a chunk-based ensemble to weigh ensemble members with the final instances in each chunk, enabling rapid tracking of data changes, achieving higher classification accuracy, and adapting to various types of concept drift. The algorithm proposed in this paper is a passive adaptive The current concept drift data stream learning methods can be divided into active detection methods and passive adaptive methods [20]. Active detection methods usually use a window mechanism to deal with concept drift.…”

Section: Concept Drift Data Streams Learning Methodsmentioning

confidence: 99%

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Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Han

Gao

et al. 2023

Applied Sciences

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The imbalance and concept drift problems in data streams become more complex in multi-class environment, and extreme imbalance and variation in class ratio may also exist. To tackle the above problems, Hybrid Sampling and Dynamic Weighted-based classification method for Multi-class Imbalanced data stream (HSDW-MI) is proposed. The HSDW-MI algorithm deals with imbalance and concept drift problems through the hybrid sampling and dynamic weighting phases, respectively. In the hybrid sampling phase, adaptive spectral clustering is proposed to sample the data after clustering, which can maintain the original data distribution; then the sample safety factor is used to determine the samples to be sampled for each class; the safe samples are oversampled and the unsafe samples are under-sampled in each cluster. If the data stream is extremely imbalanced, the sample storage pool is used to extract samples with a high safety factor to add to the data stream. In the dynamic weighting phase, a dynamic weighting method based on the G-mean value is proposed. The G-mean values are used as the weights of each base classifier in the ensemble and the ensemble is dynamically updated during the processing of the data stream to accommodate the occurrence of concept drift. Experiments were conducted with LB, OAUE, ARF, BOLE, MUOB, MOOD, CALMID, and the proposed HSDW-MI on 10 multi-class synthetic data streams with different class ratios and concept drifts and 3 real multi-class imbalanced streams with unknown drifts, and the results show that the proposed HSDW-MI has better classification capabilities and performs more consistently compared to all other algorithms.

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Section: Concept Drift Data Streams Learning Methodsmentioning

confidence: 99%

Section: Concept Drift Data Streams Learning Methodsmentioning

confidence: 99%

Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Han

Gao

et al. 2023

Applied Sciences

Self Cite

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“…It uses a time-decaying chance of drawing data instances for dealing with concept drift and gives a higher chance of drawing minority class instances for dealing with class imbalance. In [48] a window-based DESW-ID algorithm is proposed which uses three different windows for resampling with a different Poisson distribution per window. The algorithm dynamically adjusts the number of base classifiers by sorting them in terms of accuracy and applying reverse search to find the optimal number of classifiers.…”

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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“…Another method was proposed by [ 27 ] in which the data stream was resampled by Poisson distribution in the first step. Another sampling step was used by previously-stored minor-class instances if a high unbalance class state was observed.…”

Section: Literature Reviewmentioning

confidence: 99%

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Fatlawi

Kiss

2023

Sensors

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Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.

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Dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream

Cited by 8 publications

References 24 publications

Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

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

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

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