A framework for the construction of new features for hard classification tasks is discussed. The approach brings together ideas from the fields of machine learning, computational geometry, and pattern recognition. Two heuristics for evaluation of newly-constructed features are proposed, and their statistical significance verified. Finally, it is shown how the proposed framework can be used to c...