Varying Parameter in Classification Based on Imprecise Probabilities

Summary. We shall present a first explorative study of the variation of the parameter s of the imprecise Dirichlet model when it is used to build classification trees. In the method to build classification trees we use uncertainty measures on closed and convex sets of probability distributions, otherwise known as credal sets. We will use the imprecise Dirichlet model to obtain a credal set from a sample, where the set of probabilities obtained depends on s. According to the characteristics of the dataset used, we will see that the results can be improved varying the values of s.

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