Prediction of ship collision risk based on CART

Li, Yishan; Guo, Zhiqiang; Yang, Jie; Fang, Hui; Hu, Yongwu

doi:10.1049/iet-its.2018.5281

Cited by 24 publications

(8 citation statements)

References 12 publications

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“…, n is the number of samples and m is the number of features. According to the CART tree algorithm as the base classifier [34,35], the model function can be defined by Equation 2as follows:…”

Section: Xgboost Classification Machine Learning Algorithmmentioning

confidence: 99%

An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

Hao

Wang

2021

JMSE

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The reasonable decision of ship detention plays a vital role in flag state control (FSC). Machine learning algorithms can be applied as aid tools for identifying ship detention. In this study, we propose a novel interpretable ship detention decision-making model based on machine learning, termed SMOTE-XGBoost-Ship detention model (SMO-XGB-SD), using the extreme gradient boosting (XGBoost) algorithm and the synthetic minority oversampling technique (SMOTE) algorithm to identify whether a ship should be detained. Our verification results show that the SMO-XGB-SD algorithm outperforms random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithm. In addition, the new algorithm also provides a reasonable interpretation of model performance and highlights the most important features for identifying ship detention using the Shapley additive explanations (SHAP) algorithm. The SMO-XGB-SD model provides an effective basis for aiding decisions on ship detention by inland flag state control officers (FSCOs) and the ship safety management of ship operating companies, as well as training services for new FSCOs in maritime organizations.

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Section: Xgboost Classification Machine Learning Algorithmmentioning

confidence: 99%

An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

Hao

Wang

2021

JMSE

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“…In our previous work [27,28], we applied the DT algorithm in anomaly intrusion detection and found it to have excellent classification performance. The DT has the advantages of intuitive expression and convenient operation and is widely used in research [29][30][31][32][33][34][35][36]. It consists of a root node, a child node, and a leaf node.…”

Section: A Brief Description Of the Decision Tree Algorithmmentioning

confidence: 99%

Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules

Lee

Huang

et al. 2019

Applied Sciences

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Air pollution has an ongoing devastating impact on the planet, damaging ecosystems, depleting natural resources, and endangering human health. This paper proposes a new intelligent algorithm that includes parameter optimization and decision rules to forecast and analyze of urban air quality. Through analysis of 24-h daily air quality data provided by the Beijing Air Quality Monitoring Station, simulated annealing (SA) and a decision tree (DT) emerge as the key factors. We prove that in the investigated algorithm, SA and DT can be used to make decision rules and achieve better accuracy for classification. We find that SA can be used to adjust the best parameter settings for the DT. Simulation results show that the accuracy of the proposed algorithm for classification is far better than other existing approaches.

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“…Random Forest belongs to an integrated algorithm, which is based on the Bagging algorithm, and the Classification and Regression Tree (CART) algorithm [17]. The Bagging algorithm is short for Bootstrap Aggregating, and it is based on Bootstrap sampling [18].…”

Section: Random Forest Algorithm Flowmentioning

confidence: 99%

Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour

Liu¹,

Hong²,

Hu³

2019

ELEKTRON ELEKTROTECH

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Thousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging behaviours. In the charging process, big data regarding charging behaviour of EVs are generated. This paper proposes a big data mining technique based on Random Forest and Principle Component Analysis for EV charging behaviour to identify and analyse clusters with different charging characteristics from the big data. This paper uses Dundee’s January 2018 EV charging data to conduct experiments, and obtains the charging behaviour clusters of the workdays, weekends, and holidays of January. The superiority of the random forest algorithm in the EV clustering problem is reflected when compared to the Euclidean distance method. The clusters obtained by the random forest algorithm have clearer characteristics, including the user’s charging method and travel behaviour. The results show that the charging behaviour of EVs has certain regularity, and the charging load has obvious peak-to-valley difference that is necessary to be regulated.

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Prediction of ship collision risk based on CART

Cited by 24 publications

References 12 publications

An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules

Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour

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