Incremental learning using self-organizing neural grove

Abstract

Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. We proposed a novel pruning method for efficient classification and we call this model as self-organizing neural grove (SONG). In this paper, we investigate SONG's incremental learning performance. © 2010 Springer-Verlag Berlin Heidelberg.