Incremental learning using self-organizing neural grove

Summary form only given. Multiple classifier systems (MCS) have become popular during the last decade. The self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of the simple setting and fast learning. In an earlier paper, we proposed a pruning method for the structure of the SGNT in the MCS to reduce the computational cost and we called this model the self-organizing neural grove (SONG). In this paper, we investigate the performance of incremental learning using SONG for a large scale classification problem. The results show that the SONG can improve its classification accuracy as well as reducing the computational cost in incremental learning.