Application of XGBoost algorithm in the optimization of pollutant concentration

Li, Jiangtao; Xu, An; Li, Qingyong; Wang, Chao; Yu, Hongliang; Zhou, Xinyuan; Geng, Yangli-ao

doi:10.1016/j.atmosres.2022.106238

Cited by 61 publications

(17 citation statements)

References 29 publications

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“…XGBoost is an optimized distributed gradient boosting library designed to be efficient, flexible and portable ( Li et al., 2022 ). First, it constructs an appropriate number of weak learners, mainly classification regression trees, to train weak learners.…”

Section: Methodsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

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Section: Methodsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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“…Equation 10 for the combinatorial optimization task. The classification layer contains optimized instances of an XGBoost [40] and a Decision Tree [41]. The optimization layer contains instances of a parameterized Genetic Algorithm [35] and combinatorial PSO [42].…”

Section: B Proof Of Concept 2 -Compound Dssmentioning

confidence: 99%

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Oliveira

Neto

2023

IEEE Access

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The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand. However, the use of intelligent black-box models can hinder the decision support system's potential to be fully adopted because opaque processes raise suspicions and doubts among careful decision makers. Moreover, appropriate and comprehensible explanations may foster trustworthiness and allow for reasonable adjustments or even corrections. This work proposed an approach that incorporates three reasonability aspects into Decision Systems: feasibility, rationality, and plausibility. Thus, by providing decision makers with reasonable candidate solutions for a complex problem, they are expected to perform their tasks more effectively (i.e. decide with more efficiency as well as efficacy). The new approach is accompanied by two proofs of concept in the health and public security areas. Comparative results using random and rational approaches, including the simulation of distinct user profiles, are presented. The proposed approach achieved superior metrics with regard to feasibility and plausibility, suggesting that this proposition can be applied to real-world applications.

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“…The theoretical basis of XGBoost is as follows. Suppose the model has k decision trees, the integrated model can be expressed mathematically as [38]:…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

“…By adding a new tree f k to fit the residual error between the predicted value of the previous tree and the actual value, a new model is formed, and the new model is used as the basis for the next model learning. Mathematically, this can be stated as follows [38].…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

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Predicting Pavement Structural Condition Using Machine Learning Methods

et al. 2022

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State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

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Application of XGBoost algorithm in the optimization of pollutant concentration

Cited by 61 publications

References 29 publications

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Predicting Pavement Structural Condition Using Machine Learning Methods

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