Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, background): “classification and regression tree (CART) [58], J48 decision tree [59], reduced error pruning tree (REPT) [60–62], reduced error pruning tree (REPTree) [61,63], random”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, methodology): “trees (Hong et al. 2018; Tien Bui et al. 2014), alternating decision trees (Chen et al.”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, background): “It can provide It can provide solutions for two-class, multi-class single-label, multi-class multi-label, single label category, and regression problems [91]. solutions for two-class, multi-class single-label, multi-class multi-label, single label category, and regression problems [91].”
Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh (2020, methodology): “Prediction and success rate curves are generally employed for accuracy assessment and model comparison [14,60].”