Abstract Most general-purpose classification methods, such as support-vector machine (SVM) and random forest (RF), fail to account for an unusual characteristic of astronomical data: known measurement error uncertainties. In data, this information is often given in the data but discarded because popular learning classifiers cannot incorporate it. We propose a simulation-based approach that inco...