SMOTE-OB: Combining SMOTE and Online Bagging for Continuous Rebalancing of Evolving Data Streams

Bernardo, Alessio; Valle, Emanuele Della

doi:10.1109/bigdata52589.2021.9671609

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The precision rate measures the proportion of successfully predicted operating conditions to all operating cases. It can be expressed by Equation (7).…”

Section: ) Precisionmentioning

confidence: 99%

“…Yang et al [6] proposed a hybrid classifier architecture which first uses density undersampling to obtain balanced subsets of multiple categories, and then uses a costeffective classification method to deal with information incompleteness. Bernardo et al [7] proposed a SMOTEOB method that enables the dataset to be rebalanced in a continuous data stream and the classifier to be trained and updated online, thus making the model more adaptable and robust.…”

Section: Introductionmentioning

confidence: 99%

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The Fault Diagnosis of a Plunger Pump Based on the SMOTE + Tomek Link and Dual-Channel Feature Fusion

Yang,

Xu,

Wang

et al. 2024

Applied Sciences

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Mechanical condition monitoring data in real engineering are often severely unbalanced, which can lead to a decrease in the stability and accuracy of intelligent diagnosis methods. In this paper, a fault diagnosis method based on the SMOTE + Tomek Link and dual-channel feature fusion is proposed to improve the performance of the sample imbalance fault diagnosis method, taking the piston pump of a turnout rutting machine as the research object. Combining the data undersampling method and the oversampling method to redistribute the collected normal data and fault data makes the diagnostic model have better diagnostic performance in the case of insufficient fault samples. And, in order to fully utilize the global features and local features, a global–local feature complementary module (GLFC) is proposed. Firstly, the generated data similar to the original data are constructed using the SMOTE + Tomek Link method; secondly, the generated data are input into a GLFC module and BiGRU at the same time, the GLFC module extracts the spatial global features and local features of the original vibration data, and BiGRU extracts the temporal information features of the original vibration data, and fuses the extracted feature information, and inputs the fused features into the attention layer; finally, a GLFC module is proposed by the SMOTE + Tomek Link method to make full use of the global features and local features. The extracted feature information is fused, and the fused features are input to the attention layer; finally, the fault classification is completed by the softmax classifier. In this paper, the accuracy and robustness of the proposed model are demonstrated through experiments.

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“…The precision rate measures the proportion of successfully predicted operating conditions to all operating cases. It can be expressed by Equation (7).…”

Section: ) Precisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Fault Diagnosis of a Plunger Pump Based on the SMOTE + Tomek Link and Dual-Channel Feature Fusion

Yang,

Xu,

Wang

et al. 2024

Applied Sciences

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“…They are calculated over a sliding window of 500 instances. (Bernardo et al, 2020a) Data-Level RI Online Explicit C-SMOTE (Bernardo et al, 2020b) Data-Level RI Online Explicit VFC-SMOTE (Bernardo and Della Valle, 2021b) Data-Level RI Online Explicit CSARF (Loezer et al, 2020) Algo-Level CS Online Explicit -GHVFDT (Lyon et al, 2014) Algo-Level TM Online Implicit -HDVFDT (Cieslak and Chawla, 2008) Algo-Level TM Online Implicit -ARF (Gomes et al, 2017) Ensemble -Online Explicit -KUE (Cano and Krawczyk, 2020) Ensemble -Hybrid Explicit LB (Bifet et al, 2010a) Ensemble -Online Explicit OBA (Bifet et al, 2009) Ensemble -Online Explicit SRP (Gomes et al, 2019) Ensemble -Online Explicit ESOS-ELM (Mirza et al, 2015) Ensemble -Chunk Explicit -CALMID (Liu et al, 2021) Ensemble -Hybrid Explicit MICFOAL (Liu et al, 2021) Ensemble -Online Explicit ROSE Ensemble -Hybrid Explicit OADA (Wang and Pineau, 2016) Ensemble -Online Explicit OADAC2 (Wang and Pineau, 2016) Ensemble -Online Explicit ARFR (Ferreira et al, 2019) Ensemble RI Online Explicit -SMOTE-OB (Bernardo and Della Valle, 2021a) Ensemble RI Online Explicit OSMOTE (Wang and Pineau, 2016) Ensemble RI Online Explicit OOB Ensemble ROB Online Implicit UOB Ensemble RUB Online Implicit ORUB (Wang and Pineau, 2016) Ensemble RUB Online Explicit OUOB (Wang and Pineau, 2016) Ensemble RB Online Explicit Kappa is used to evaluate classifiers in imbalanced settings (Brzeziński et al, , 2019. It evaluates the classifier performance by computing the inter-rater agreement between the successful predictions and the statistical distribution of the data classes, correcting agreements that occur by mere statistical chance.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Furthermore, imbalanced streams can have other underlying difficulties, such as small sample size, borderline and rare instances, overlapping among classes, or noisy labels (Santos et al, 2022). Imbalanced data streams are usually handled via class resampling (Korycki and Krawczyk, 2020;Bernardo et al, 2020b;Bernardo and Della Valle, 2021a), algorithm adaptation mechanism (Loezer et al, 2020;Lu et al, 2020), or ensembles Cano and Krawczyk, 2022). While there are several works on how to handle imbalanced data streams, there are no agreed-upon standards, benchmarks, or good practices that are necessary for fully reproducible, transparent, and impactful research.…”

mentioning

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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An online ensemble classification algorithm for multi-class imbalanced data stream

Han,

Li,

Meng

et al. 2024

Knowl Inf Syst

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SMOTE-OB: Combining SMOTE and Online Bagging for Continuous Rebalancing of Evolving Data Streams

Cited by 8 publications

References 26 publications

The Fault Diagnosis of a Plunger Pump Based on the SMOTE + Tomek Link and Dual-Channel Feature Fusion

The Fault Diagnosis of a Plunger Pump Based on the SMOTE + Tomek Link and Dual-Channel Feature Fusion

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

An online ensemble classification algorithm for multi-class imbalanced data stream

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