Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm

Zeng, Li; Huang, Wenchao; Xiong, Yan; Ren, Siqi; Zhu, Tuanfei

doi:10.1016/j.knosys.2020.105694

Cited by 57 publications

(32 citation statements)

References 26 publications

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“…This pattern signifies a synthesized vision of data records that were analyzed in the past and it progressively analysis for the availability of new records. On-line and incremental learning methods are used for dealing with the rapid arrival of continuous data, unbounded streams, and time-varying data [24][25][26]. Online environment is a non-stationary one that copes with the learning issues in big data conditions [27].…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning Framework for Mining Big Data Stream

Eisa¹,

El-Rashidy²,

Alshehri³

et al. 2022

Computers, Materials &Amp; Continua

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At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model. The proposed model is implemented in spark architecture. For each data stream, the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation. This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency. The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy (0.861) precision (0.9328) and minimal MSE (0.0416).

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Section: Introductionmentioning

confidence: 99%

Incremental Learning Framework for Mining Big Data Stream

Eisa¹,

El-Rashidy²,

Alshehri³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…An error cost function model was designed to guide the Convolutional Neural Network (CNN) parameters optimization in the direction of feature classification and was applied to the heavy-duty industrial robot system diagnosis procedure [19]. By using ensemble learning, Li et al proposed a Dynamic Updated Ensemble (DUE) for learning imbalanced data streams with concept drift [20].…”

Section: Introductionmentioning

confidence: 99%

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

et al. 2021

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In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

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“…Compared to online methods, the model learns faster at the beginning of training in a chunk-based approach since the model is seeded with a big initial chunk of data that makes the update process more effective; however, a concept drift may occur later in the learning process, and if the drift is located within a chunk, it is possible that it will be missed, resulting in a significant reduction in model accuracy. In the case of online learning, the latter problem does not affect the performance of the model; however, the model may suffer from slower initial learning performance [37].…”

Section: Introductionmentioning

confidence: 99%

“…Another problem of such a model is its runtime. Compared to the chunk-based models, online learning methods are less efficient and require more computations [37].…”

Section: Introductionmentioning

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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Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm

Cited by 57 publications

References 26 publications

Incremental Learning Framework for Mining Big Data Stream

Incremental Learning Framework for Mining Big Data Stream

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

A Broad Ensemble Learning System for Drifting Stream Classification

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