Incremental learning using self-organizing neural grove

Multiple classifier systems (MCS) have become popular during the last decade. 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 as self-organizing neural grove (SONG). In this paper, we investigate a performance of incremental learning using SONG for a large-scale classification problem. The results show that the SONG can ensure rapid and efficient incremental learning.