HIE-EDT: Hierarchical interval estimation-based evidential decision tree.

Decision tree algorithm, because of its strong interpretability and high algorithm efficiency, is widely used in the field of pattern recognition and classification. When the number of data samples is small and there is uncertainty in the data, it is difficult for the traditional decision tree algorithm to fully mine the effective information in the data. In this paper, we use the Dempster–Shafer framework to model data uncertainty and propose a hierarchical interval estimation method to improve decision tree algorithms. The proposed method constructs intervals through two methods of attribute boundary and mean square error estimation, which not only utilizes the characteristics of intervals to model the inaccuracy of data, but also constrains intervals from two aspects, narrowing the representation range of available information. By comparing with the classic decision tree algorithm and the decision tree algorithm based on single interval estimation, the proposed method can perform classification tasks robustly and accurately in different types of data under seven data sets. • We propose an evidential decision tree based on a reliability-based BPA generation method and hierarchical interval estimation. • The proposed method greatly reduces the influence of the labels of uncertain data on the classification accuracy of the decision tree and strengthens the robustness of the algorithm. • Comparison with fifteen different decision trees, the accuracy of our proposed method on seven datasets is higher than other methods especially in the face of with uncertain attributes and labels. [ABSTRACT FROM AUTHOR]