Ensemble learning approach based on stacking for unmanned surface vehicle's dynamics

Chen, Cheng; Xu, Pengfei; Cheng, Hong; Ding, Yanxu; Zheng, Jinhai; Ge, Tida; Sun, Danfeng; Xu, Jin

doi:10.1016/j.oceaneng.2020.107388

Cited by 18 publications

(6 citation statements)

References 22 publications

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“…Authors confirmed its applicability for the manipulation of non-stationary data in real-time applications, outperforming a range of well-known QoE predictors in terms of accuracy and processing complexity. Finally, work in Cheng et al (2020) proposed a stacking-based ensemble ML scheme for predicting the complex dynamics of unmanned surface vehicles. In this case, authors made use of treebased ensemble models, as well as DL techniques for producing different combinations of stacked classifiers, resulting in a notably improved accuracy in comparison with other state-of-the-art techniques.…”

Section: Hierarchical Stacking-based Ensemble MLmentioning

confidence: 99%

Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing

Sánchez-Iborra¹,

Zoubir²,

Hamdouchi³

et al. 2023

Informatica

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The coordinated integration of heterogeneous TinyML-enabled elements in highly distributed Internet of Things (IoT) environments paves the way for the development of truly intelligent and context-aware applications. In this work, we propose a hierarchical ensemble TinyML scheme that permits system-wide decisions by considering the individual decisions made by the IoT elements deployed in a certain scenario. A two-layered TinyML-based edge computing solution has been implemented and evaluated in a real smart-agriculture use case, permitting to save wireless transmissions, reduce energy consumption and response times, at the same time strengthening data privacy and security.

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Section: Hierarchical Stacking-based Ensemble MLmentioning

confidence: 99%

Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing

Sánchez-Iborra¹,

Zoubir²,

Hamdouchi³

et al. 2023

Informatica

View full text Add to dashboard Cite

show abstract

“…18 Additionally, the stacking model has been utilized to assess the status of unmanned surface vehicles and predict anomalies in tobacco leaf quality and iron quality. [19][20][21] The efficacy of these approaches has been validated through the utilization of diverse datasets from different fields. Researchers have discovered that stacking ensemble methods exhibit superior performance compared to commonly used machine learning methods in bankruptcy prediction models.…”

Section: Stacking Algorithm Modelsmentioning

confidence: 99%

“…Parameter estimation has been enhanced through the combination of support vector machine (SVM) + superposition models with diverse expert opinions 18 . Additionally, the stacking model has been utilized to assess the status of unmanned surface vehicles and predict anomalies in tobacco leaf quality and iron quality 19‐21 . The efficacy of these approaches has been validated through the utilization of diverse datasets from different fields.…”

Section: Related Workmentioning

confidence: 99%

Joint loan risk prediction based on deep learning‐optimized stacking model

Wang,

Pan

et al. 2023

Engineering Reports

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In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results.

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“…ough the model stacking has been used in various studies [52] to provide a solution for the classification problems, its performance is restricted to SFC as it considers only one target variable for decision making. In this study, we have enhanced the capability of the model stacking approach to provide MFC and make it context-free.…”

Section: Incident Detection and Classificationmentioning

confidence: 99%

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

et al. 2021

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Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

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Ensemble learning approach based on stacking for unmanned surface vehicle's dynamics

Cited by 18 publications

References 22 publications

Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing

Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing

Joint loan risk prediction based on deep learning‐optimized stacking model

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

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