From circular ordinal regression to multilabel classification

Several applications domains like wind forecasting in meteorology and robot control in robotics demand for learning algorithms that are able to make discrete directional predictions. We refer to this problem setting as circular ordinal regression, since it shares the same properties as traditional ordinal regression, namely the need for a specific model structure and order-preserving loss functions. This article gives a detailed introduction to the topic and proposes two methods. The first one is a circular support vector approach (cSVM), parameterized with only two vectors. The second method converts circular ordinal regression to a multilabel classification approach that takes the circular ranking into account by minimizing the Hamming loss. We also present initial empirical results based on two toy examples and a real-life application in the area of brain-computer interfaces.