Fault Diagnosis of Aircraft Actuators Based on AdaBoost-ASVM

Wei, Ruonan; Jiang, Ju; Xu, Haiyan; Sun, Xiaoyun; Chen, Yifei

doi:10.1007/978-981-15-8155-7_12

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…This innovative approach not only tackled class imbalance effectively but also diversified sample distribution, thereby augmenting the stability and generalizability of the AdaBoost framework. Further advancing the field, Wei et al [46] developed a fault diagnosis algorithm aimed at enhancing the precision in detecting actuator failures within the closed-loop control systems of aircraft. This algorithm utilizes an aggregate experience model decomposition method along with principal component analysis (PCA) to extract failure features, followed by the application of an adaptive SVM method within the AdaBoost framework for classification.…”

Section: Imbalanced Binary Classification: Svm and Adaboost Approachesmentioning

confidence: 99%

Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification

Jaballi,

Zrigui,

Nicolas

et al. 2024

Preprint

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This study confronts the challenge of analyzing multilingual, code-switched conversations during the COVID-19 pandemic, a context where traditional classifiers often fall short. We developed a cost-sensitive ensemble learning approach that combines a reweighted AdaBoost-SVM model with an SVM as its base learner, specifically designed to effectively manage the imbalanced dataset common in code-switched communication scenarios. A key innovation of our approach is the novel rebalancing of AdaBoost weights. By incrementally adjusting the weights of misclassified samples from both minority and majority classes, we achieve a more balanced classification in each iteration. This strategy significantly improves the accuracy for minority class classification, a common issue with existing models. In the testing phase, we employed a comprehensive selection of both machine and deep learning classifiers, including Naive Bayes, Decision Trees, SMOTEBoost, CNN, Bi-LSTM, etc. These classifiers underwent comprehensive evaluation across two different multilingual datasets, assessed using six distinct metrics, including P-mean. The results from our experiments demonstrate that our proposed ensemble learning approach, fine-tuned with optimal hyperparameters and leveraging M-BERT for feature extraction, achieved remarkable accuracies of 78.84%, 86.56% and 83.96% on the test sets of the CTSA, TUNIZI and combined CTSA-TUNIZI datasets, respectively. This performance not only surpassed traditional classification methods but also outperformed advanced deep learning models, such as Bi-LSTM.

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Section: Imbalanced Binary Classification: Svm and Adaboost Approachesmentioning

confidence: 99%

Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification

Jaballi,

Zrigui,

Nicolas

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Tis not only enhanced the sample distribution variety but also made the AdaBoost integration more stable and generalizable. Wei et al [47] proposed a fault diagnosis algorithm to address the problem of poor accuracy of actuator failure identifcation under airplane closed-loop control. Te algorithm extracted failure features using the aggregate experience model decomposition method and principal component analysis (PCA).…”

Section: Related Workmentioning

confidence: 99%

Rebalance Weights AdaBoost‐SVM Model for Imbalanced Data

Piao

Wang

Yuan

2023

Computational Intelligence and Neuroscience

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Classification of imbalanced data is a challenging task that has captured considerable interest in numerous scientific fields by virtue of the great practical value of minority accuracy. Some methods for improving generalization performance have been developed to address this classification situation. Here, we propose a cost-sensitive ensemble learning method using a support vector machine as a base learner of AdaBoost for classifying imbalanced data. Considering that the existing methods are not well studied in terms of how to precisely control the classification accuracy of the minority class, we developed a novel way to rebalance the weights of AdaBoost, and the weights influence the base learner training. This weighting strategy increases the sample weight of the misclassified minority while decreasing the sample weight of the misclassified majority until their distributions are even in each round. Furthermore, we included P-mean as one of the assessment markers and discussed why it is necessary. Experiments were conducted to compare the proposed and comparison 10 models on 18 datasets in terms of six different metrics. Through comprehensive experimental findings, the statistical study is performed to verify the efficacy and usability of the proposed model.

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Fault Diagnosis of Aircraft Actuators Based on AdaBoost-ASVM

Cited by 2 publications

References 10 publications

Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification

Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification

Rebalance Weights AdaBoost‐SVM Model for Imbalanced Data

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