The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and interval-valued functional data are considered as inputs in an ordinal classification problem. Six ordinal classifiers for interval data and interval-valued functional data are proposed. Three of them are parametric, one of them is based on ordinal binary decompositions and the other two are based on ordered logistic regression. The other three methods are based on the use of distances between interval data and kernels on interval data. One of the methods uses the weighted $k$-nearest-neighbor technique for ordinal classification. Another method considers kernel principal component analysis plus an ordinal classifier. And the sixth method, which is the method that performs best, uses a kernel-induced ordinal random forest. They are compared with na\"ive approaches in an extensive experimental study with synthetic and original real data sets, about human global development, and weather data. The results show that considering ordering and interval-valued information improves the accuracy. The source code and data sets are available at https://github.com/aleixalcacer/OCFIVD.

翻译：序分类的目标是根据一组观测输入预测输出的有序标签。区间值数据指以区间形式呈现的数据。本研究首次将区间值数据及区间值函数型数据作为序分类问题的输入。针对区间数据与区间值函数型数据，提出了六种序分类器。其中三种为参数化方法，一种基于有序二元分解，另外两种基于有序逻辑回归。其余三种方法基于区间数据间距离及区间核函数：一种方法采用加权$k$-近邻技术实现序分类，另一种方法结合核主成分分析与序分类器，第六种性能最优的方法采用核诱导有序随机森林。通过与朴素方法在涵盖人类全球发展、气象数据的合成及真实原始数据集上的广泛实验对比，结果表明考虑有序性和区间值信息能提升分类准确率。源代码与数据集可通过 https://github.com/aleixalcacer/OCFIVD 获取。