A Hybrid Nearest-Neighbor and Nearest-Hyperrectangle Algorithm

Algorithms based on Nested Generalized Exemplar (NGE) theory 10] classify new data points by computing their distance to the nearest \generalized exemplar" (i.e. an axis-parallel multidimensional rectangle). An improved version of NGE, called BNGE, was previously shown to perform comparably to the Nearest Neighbor algorithm. Advantages of the NGE approach include compact representation of the training data and fast training and classiication. A hybrid method that combines BNGE and the k-Nearest Neighbor algorithm, called KBNGE, is introduced for improved classiication accuracy. Results from eleven domains show that KBNGE achieves generalization accuracies similar to the k-Nearest Neighbor algorithm at improved classiication speed. KBNGE is a fast and easy to use inductive learning algorithm that gives very accurate predictions in a variety of domains and represents the learned knowledge in a manner that can be easily interpreted by the user.