On Adaptive Boosting for System Identification

Bjurgert, Johan; Valenzuela, Patricio E.; Rojas, Cristian R.

doi:10.1109/tnnls.2017.2754319

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Expected to address the preceding challenges, a novel method using improved LightGBM is proposed in this research for the fault detection of wind turbine gearboxes. Within our method, the improved LightGBM has a lower false alarm rate and lower missing detection rate compared with the GBDT, XGBoost, LightGBM [20][21][22]. An improved LightGBM which combines Bayesian hyper-parameter optimization and the LightGBM algorithm is proposed to diagnose faults and to provide a novel method for monitoring and fault diagnosis of wind turbine gearboxes [23].…”

Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

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Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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“…We also chose a tree-based ensemble classification algorithm (Random Forest) and Adaptive Boost (AdaBoost) to build models based on the combination of the aforementioned variables. Decision tree and Naive Bayes produce models with interpretable structures, whereas Random Forest, AdaBoost and SVM are "black box" models, where the function connecting the predictor variables with response is opaque to the user (20)(21)(22)(23)(24)(25)(26). Predictive performance as previously described ( 27), was assessed by the area under the receiver operating characteristic (ROC) curve [AUC (28)], calibration curve and the Lift curve (30% of the original cohort, randomly selected samples).…”

Section: Algorithmsmentioning

confidence: 99%

Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data

Guo

Zhang

et al. 2021

Transl Pediatr

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Background: Postoperative blood coagulation assessment of children with congenital heart disease (CHD) has been developed using a conventional statistical approach. In this study, the machine learning (ML) was used to predict postoperative blood coagulation function of children with CHD, and assess an array of ML models. Methods: This was a retrospective and data mining study. Based on the samples of 1,690 children with CHD, and screening data based on demographic characteristics, conventional coagulation tests (CCTs) and complete blood count (CBC), with a precise data selection process, and the support of data mining and ML algorithms including Decision tree, Naive Bayes, Support Vector Machine (SVM), Adaptive Boost (AdaBoost) and Random Forest model, and explored the best prediction models of postoperative blood coagulation function for children with CHD by models performance measured in the area under the receiver operating characteristic (ROC) curve (AUC), calibration or Lift curves, and further verified the reliability of the models with statistical tests.Results: In primary objective prediction, as decision tree, Naive Bayes, SVM, the AUC of our prediction algorithm was 0.81, 0.82, 0.82, respectively. The accuracy rate of the overall forecast has reached more than 75%. Subsequently, we furtherly build improved models. Among them, the true positive rate of the AdaBoost, Random Forest and SVM prediction models reached more than 80% in the ROC curve. These overall accuracy rate indicated a good classification model. Combined calibration curves and Lift curves, the better fit is the SVM model, which predicted postoperative abnormal coagulation, Lift =2.2, postoperative normal coagulation, Lift =1.8. The statistical results furtherly proved the reliability of ML models. The age, sex, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), white blood cell count (WBC) and platelet count (PLT) were the key features for predicting the postoperative blood coagulation state of children with CHD. Conclusions: ML technology and data mining algorithms may be used for outcome prediction in children with CHD for postoperative blood coagulation state based on the bulk of clinical data, especially CBC indictors from the real world.

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“…AdaBoost and its variants have been widely applied to regression and classification problems. Bjurgert et al [35] used AdaBoot to estimate dynamical systems and explored the connection between AdaBoost and system identification. Qi et al [36] applied ensemble learning strategy for the learning problem with label proportions (LLPs).…”

Section: A Ensemble Learningmentioning

confidence: 99%

Deep Decision Tree Transfer Boosting

Jiang

Mao

Ding

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

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Instance transfer approaches consider source and target data together during the training process, and borrow examples from the source domain to augment the training data, when there is limited or no label in the target domain. Among them, boosting-based transfer learning methods (e.g., TrAdaBoost) are most widely used. When dealing with more complex data, we may consider the more complex hypotheses (e.g., a decision tree with deeper layers). However, with the fixed and high complexity of the hypotheses, TrAdaBoost and its variants may face the overfitting problems. Even worse, in the transfer learning scenario, a decision tree with deep layers may overfit different distribution data in the source domain. In this paper, we propose a new instance transfer learning method, i.e., Deep Decision Tree Transfer Boosting (DTrBoost), whose weights are learned and assigned to base learners by minimizing the data-dependent learning bounds across both source and target domains in terms of the Rademacher complexities. This guarantees that we can learn decision trees with deep layers without overfitting. The theorem proof and experimental results indicate the effectiveness of our proposed method.

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On Adaptive Boosting for System Identification

Cited by 5 publications

References 10 publications

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data

Deep Decision Tree Transfer Boosting

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