A Prediction Method Based on Extreme Gradient Boosting Tree Model and its Application

Lao, Yongchang; Qi, Fangzhong; Zhou, Jiakai; Fang, Xiaobao

doi:10.1088/1742-6596/1995/1/012017

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…This study used XGB. The algorithm is a type of gradient-boosting algorithm that uses decision trees as base learners [19]. It works by iteratively adding decision trees to the model, with each tree attempting to correct the errors made by the previous tree [20].…”

Section: Extreme Gradient Boosting Decision Tree Modelmentioning

confidence: 99%

The Influence of Transportation Accessibility on Traffic Volumes in South Korea: An Extreme Gradient Boosting Approach

Lee,

Yang,

Cho

et al. 2023

Urban Science

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This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected from the Korea Transport Institute, Statistics Korea, and National Spatial Data Infrastructure Portal. Several key findings of feature importance and plots in non-linear relationships are as follows: First, accessibility indicators exhibited around 5 to 10% of feature importance except for Mart (around 50%). Second, better accessibility to public transportation infrastructures, such as bus stops and transit stations, was associated with higher annual average daily traffic (AADT), particularly in metropolitan areas including Seoul and Busan. Third, access to large-scale markets may have unintended effects on traffic volumes for both vehicles and automobiles. Fourth, it was shown that lower rates of AADT were associated with higher accessibility to elementary schools for all three modes of transportation. This study contributes to (1) understanding complex relationships between the variables, (2) emphasizing the role of transportation accessibility in transportation plans and policies, and (3) offering relevant policy implications.

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Section: Extreme Gradient Boosting Decision Tree Modelmentioning

confidence: 99%

The Influence of Transportation Accessibility on Traffic Volumes in South Korea: An Extreme Gradient Boosting Approach

Lee,

Yang,

Cho

et al. 2023

Urban Science

View full text Add to dashboard Cite

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“…Compared with ordinary ID3 and C4.5, the main feature of CART tree is that it is a binary tree, and the feature value of each node is "yes" and "no". Such a decision tree recursively divides each feature and determines a unique output in each partition of the input space [11].…”

Section: Prediction Modulementioning

confidence: 99%

Warning of Dangerous Driving Behavior Caused by Drivers and Road Environmental Factors

Yang

Lin

2022

Frontiers in Artificial Intelligence and Applications

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With the development of the supply-side reform of road transportation in China and the rapid development of public transportation, the pressure of public security management is also increasing. According to the statistics of public transport safety accidents, the number of public transport accidents and deaths accounts for about 1% of the total number of relevant road traffic accidents in China, among which 93% of bus accidents are caused by drivers’ traffic violations. And, therefore, in order to predict the dangerous driving behavior of public transportation driver, this project combines the driver factors and environmental factors such as weather, road conditions, using the gradient promotion tree algorithm to forecast the risk in the process of public transportation to drive, to achieve the advance management and intervention of public traffic safety, to avoid major public traffic safety accidents, It provides ideas and deployment direction for public security traffic management.

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“…Gradient boosting tree (GBT) is based on the boosting idea in ensemble learning and runs in serial mode [19][20][21]. In each iteration, a weak learner (decision tree T 1 ∼T m ) is selected, and a new decision tree is established in the gradient direction of reducing the residual (F 0 ∼F m ).…”

Section: Gradient Boosting Treementioning

confidence: 99%

Human Resource Matching Support System Based on Deep Learning

Chen

2022

Mathematical Problems in Engineering

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Aiming at the problem of reasonable recommendation and accurate matching of human resources, a hybrid human resources matching recommendation algorithm based on GBT-CNN is proposed in this article. The advantages of traditional GBT and CNN algorithms are combined and can give full play to the high-level feature abstraction ability of convolution processing. The gradient lifting tree is used to transform the features, complete the feature screening and coding, and then input the hybrid convolution neural network to obtain the high-dimensional feature abstraction by using the hybrid convolution operation, to improve the quality of human resources recommendation. In this article, GBT and CNN algorithms are first described, and then the basic framework and specific implementation of GBT-CNN algorithm are introduced. Finally, the effectiveness of the algorithm is verified by simulation experiments. The results show that the deeper correlation between job seeker information and job information can be effectively captured by the algorithm. Besides, reasonable recommendation and accurate matching of human resources can be realized.

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A Prediction Method Based on Extreme Gradient Boosting Tree Model and its Application

Cited by 4 publications

References 12 publications

The Influence of Transportation Accessibility on Traffic Volumes in South Korea: An Extreme Gradient Boosting Approach

The Influence of Transportation Accessibility on Traffic Volumes in South Korea: An Extreme Gradient Boosting Approach

Warning of Dangerous Driving Behavior Caused by Drivers and Road Environmental Factors

Human Resource Matching Support System Based on Deep Learning

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