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

Bingjie Gao, Qianli Zhou, Yong Deng

Abstract: Highlights•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.