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Communication Dans Un Congrès Année : 2021

Cautious Random Forests: a new decision strategy and some experiments

Résumé

Random forest is an accurate classification strategy, which estimates the posterior probabilities of the classes by averaging frequencies provided by trees. When data are scarce, this estimation becomes difficult. The Imprecise Dirichlet Model can be used to make the estimation robust, providing intervals of probabilities as outputs. Here, we propose a new aggregation strategy based on the theory of belief functions. We also propose to assign weights to the trees according to their amount of uncertainty when classifying a new instance. Our approach is compared experimentally to the baseline approach on several datasets.
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Dates et versions

hal-03341295 , version 1 (10-09-2021)

Identifiants

  • HAL Id : hal-03341295 , version 1

Citer

Haifei Zhang, Benjamin Quost, Marie-Helène Masson. Cautious Random Forests: a new decision strategy and some experiments. 12th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2021), Jul 2021, Granada, Spain. pp.369-372. ⟨hal-03341295⟩
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