Many of the best statistical classification algorithms are binary classifiers, that is they can only distinguish between one of two classes. The number of possible ways of generalizing binary classification to multi-class increases exponentially with the number of classes. There is some indication that the best method of doing so will depend on the dataset. As such, we are particularly interested in data-driven solution design, whether based on prior considerations or on empirical examination of the data. Here we demonstrate how a recursive control language can be used to describe a multitude of different partitioning strategies in multi-class classification, including those in most common use. We use it both to manually construct new partitioning configurations as well as to examine those that have been automatically designed. Eight different strategies are tested on eight different datasets using both support vector machines (SVM) as well as logistic regression as the base binary classifiers. Numerical tests suggest that a one-size-fits-all solution consisting of one-versus-one is appropriate for most datasets however one dataset benefitted from the techniques applied in this paper. The best solution exploited a property of the dataset to produce an uncertainty coefficient 36\% higher (0.016 absolute gain) than one-vs.-one. Adaptive solutions that empirically examined the data also produced gains over one-vs.-one while also being faster.