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- W3211538081 abstract "We propose a robust decision tree induction method that mitigates the problemsof instability and poor generalization on unseen data. In the spirit of modelimprecision and robust statistics, we generalize decision trees by replacing internalnodes with two types of ensemble modules that pool together a set of decisions intoa soft decision: (1) option modules consisting of all reasonable variable choices ateach step of the induction process, (2) robust split modules including all elementsof a neighbourhood of an optimal split-point as reasonable alternative split-points.We call the resulting set of trees cultivated random forest as it corresponds to anensemble of trees which is centered around a single tree structure, alleviating the lossof interpretability of traditional ensemble methods. The explicit modelling of nonprobabilistic uncertainty about the tree structure also provides an estimate of thereliability of predictions, allowing to abstain from predictions when the uncertaintyis too high. On a variety of benchmark datasets, we show that our method is oftencompetitive with random forests, while being structurally substantially simpler andeasier to interpret." @default.
- W3211538081 created "2021-11-22" @default.
- W3211538081 creator A5028359392 @default.
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- W3211538081 date "2021-11-11" @default.
- W3211538081 modified "2023-09-23" @default.
- W3211538081 title "Cultivated Random Forests: Robust Decision TreeLearning through Tree Structured Ensembles" @default.
- W3211538081 doi "https://doi.org/10.5282/ubm/epub.77861" @default.
- W3211538081 hasPublicationYear "2021" @default.
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