AIB2

Abstract

Data reduction improves the efficiency of k-NN classifier on large datasets since it accelerates the classification process and reduces storage requirements for the training data. IB2 is an effective data reduction technique that selects some training items form the initial dataset and uses them as representatives (prototypes). Contrary to many other data reduction techniques, IB2 is a very fast, one-pass method that builds its reduced (condensing) set in an incremental manner. New training data can update the condensing set without the need of the "old" removed items. This paper proposes AIB2, a variation of IB2, which generates new prototypes instead of selecting them. AIB2 attempts to improve the efficiency of IB2 by positioning the prototypes in the center of the data areas they represent. The experimental study shows that AIB2 performs better than IB2.