SONG: Self-Organizing Neural Grove

抄録

Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN), which were proposed by Wen et al., 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 called this model as self-organizing neural grove (SONG). In this paper, I introduce the SONG for researchers of self-organizing maps (SOM). Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.