
Deep Ordinal Regression Forests
Ordinal regression is a type of regression techniques used for predictin...
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TreeStructured Scale Effects in Binary and Ordinal Regression
In binary and ordinal regression one can distinguish between a location ...
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Training Big Random Forests with Little Resources
Without access to large compute clusters, building random forests on lar...
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Uncertainty Quantification in Ensembles of Honest Regression Trees using Generalized Fiducial Inference
Due to their accuracies, methods based on ensembles of regression trees ...
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ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
We introduce the C++ application and R package ranger. The software is a...
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Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules
Deep learningbased methods have achieved promising performance in early...
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Linear Aggregation in Treebased Estimators
Regression trees and their ensemble methods are popular methods for non...
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Ordinal Trees and Random Forests: ScoreFree Recursive Partitioning and Improved Ensembles
Existing ordinal trees and random forests typically use scores that are assigned to the ordered categories, which implies that a higher scale level is used. Versions of ordinal trees are proposed that take the scale level seriously and avoid the assignment of artificial scores. The basic construction principle is based on an investigation of the binary models that are implicitly used in parametric ordinal regression. These building blocks can be fitted by trees and combined in a similar way as in parametric models. The obtained trees use the ordinal scale level only. Since binary trees and random forests are constituent elements of the trees one can exploit the wide range of binary trees that have already been developed. A further topic is the potentially poor performance of random forests, which seems to have ignored in the literature. Ensembles that include parametric models are proposed to obtain prediction methods that tend to perform well in a wide range of settings. The performance of the methods is evaluated empirically by using several data sets.
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