Evaluating the effects of distance metrics on a NGE-based system

The nested generalized exemplar (NGE) model (implemented by EACH algorithm) is an incremental form of inductive learning from examples that generalizes a given training set into hypotheses represented as a set of hyper-rectangles in an n-dimensional Euclidean space. NGE depends heavily on the distance metric used in both processes, learning and classification. This work investigates the impact on the predictive accuracy of the learnt concepts by NGE as a consequence of using three new heterogeneous distance functions namely HVDM, IVDM and WVDM, instead of the Euclidean distance metric originally proposed. The paper presents and analyses the results of experiments in various domains using the Euclidean and the three heterogeneous distance functions.