Title: Learning from Interval-valued Data. Abstract: The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations’ features are described by intervals, is also a common type of data in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals. In this paper, we focus on a highly challenging problem called learning from interval-valued data (LIND), where we aim to learn a classifier with high performance on interval-valued observations. First, we obtain the estimation error bound of the LIND problem based on Rademacher complexity. Then, we give the theoretical analysis to show the strengths of multi-view learning on classification problems, which inspires us to construct a new framework called multi-view interval information extraction (Mv-IIE) approach for improving classification accuracy on interval-valued data. The experiment comparisons with several baselines on both synthetic and real-world datasets illustrate the superiority of the proposed framework in handling interval-valued data. Moreover, we describe an application of the Mv-IIE framework that we can prevent data privacy leakage by transforming crisp-valued (raw) data into interval-valued data.