Classification Algorithm of Urban Point Cloud Data based on LightGBM

Shi, Xiaosong; Cheng, Yinglei; Dong, Xue

doi:10.1088/1757-899x/631/5/052041

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Their study concluded that the XGBoost method achieves high accuracy with fewer features. Shi et al, (2019) evaluated the performance of the LightGBM method in the classification of airborne LiDAR point cloud data in urban areas [22] and showed the effectiveness of EL approaches.…”

Section: Bagging Boostingmentioning

confidence: 99%

“…The LightGBM algorithm is a gradient boosting framework proposed based on the series of DTs [48]. LightGBM algorithm can effectively reduce the amount of computation while ensuring good accuracy [22,39,48]. An essential difference of the LightGBM framework from other decision-tree-based EL methods is related to the tree growth procedure.…”

Section: Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

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Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

et al. 2021

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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.

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Section: Bagging Boostingmentioning

confidence: 99%

Section: Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

et al. 2021

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“…In this study, a bagging method named RF and two boosting methods such as LightGBM and XGBoost were selected as classifiers. RF, XGboost and LightGBM have also been applied for point cloud classification in recent years [18], [43], [17].…”

Section: F Machine Learning Classifiersmentioning

confidence: 99%

Explainable Artificial Intelligence for Machine Learning-Based Photogrammetric Point Cloud Classification

Atik,

Duran,

Seker

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Point clouds are one of the widely used data sources for spatial modeling. Artificial intelligence approaches have become an important tool for understanding and extracting semantic information of point clouds. In particular, the explainability of machine learning approaches for 3D data has not been sufficiently investigated. Moreover, existing studies are generally limited to object classification issues. This is a pioneer study that addresses the classification of photogrammetric point clouds in terms of explainable artificial intelligence. In this study, the explainability of black-box machine learning models in the context of the classification of photogrammetric point clouds was investigated. Each point in the point cloud is defined using geometric and spectral features. Additionally, the effect of selecting the most important of these features on the classification performance of ML models such as Random Forest, XGBoost and LightGBM was examined. The explainability of ML models was analyzed with Shapley Additive exPlanation (SHAP), an explainable artificial intelligence approach. SHAP analysis was compared with filterbased Information Gain (IG) and ReliefF methods for feature selection. Using the features selected with SHAP analysis, overall accuracy (OA) of 85.50% in the Ankeny dataset, 91.70% in the Building dataset and 83.28% in the Cadastre dataset was achieved with LightGBM. The evaluation with XGBoost shows overall accuracy of 85.22% for Ankeny, 91.21% for Building and 82.47% for Cadastre. The evaluation with RF shows overall accuracy of 83.70% for Ankeny, 89.08% for Building and 79.36% for Cadastre.

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“…LightGBM is a histogram-based algorithm. Since the training time of decision trees is directly proportional to the calculation and therefore the number of splits, LightGBM both shortens the training time and reduces resource usage [23].…”

Section: Lightgbmmentioning

confidence: 99%

The Diagnosis of Diabetes Mellitus with Boosting Methods

Koçak

Çetin

2023

ECJSE

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In addition to the damage it can cause to various organs, diabetes mellitus (DM) also increases a person's risk of developing other serious health conditions. These can include heart disease, stroke, and nerve damage. Furthermore, DM is a leading cause of blindness and kidney failure. However, with proper management and treatment, many of the complications of DM can be prevented or delayed. Thus, early detection and treatment of DM are crucial. With the advancement of machine learning technology, new opportunities have emerged in the field of medicine. Many disease detection research rely on machine learning techniques, with a particular emphasis on boosting algorithms. Boosting algorithms are used to improve the accuracy of predictions made by other weak models such as decision trees. Using knowledge discovery methods, boosting algorithms are examined and compared on a diabetes dataset in this study. The performance of the boosting algorithms is evaluated by generating ROC curves and comparing average accuracy values. When the study's results were evaluated in terms of precision, Gradient Boosting, AdaBoost, CatBoost, LightGBM, and XGBoost algorithms gives success rates of %85, %83, %88, %86, and %87, respectively.

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Classification Algorithm of Urban Point Cloud Data based on LightGBM

Cited by 7 publications

References 4 publications

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Explainable Artificial Intelligence for Machine Learning-Based Photogrammetric Point Cloud Classification

The Diagnosis of Diabetes Mellitus with Boosting Methods

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