In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision. This paper proposes an Evidential Decision Tree, and an Evidential Random Forest. These two models use a distance and a degree of inclusion to allow the model to group observations whose response elements are included in each other into a single node. Experimental results showed better performance for the presented methods compared to other evidential models and to recent Cautious Random Forests when the data is noisy. The models also offer a better robustness to the overfitting effect when using datasets that are effectively uncertainly and imprecisely labeled by the contributors. The proposed models are also able to predict rich labels, an information that can be used in other approaches, such as active learning.