Competitive neural trees for pattern classification

This paper presents competitive neural trees (CNeT's) for pattern classification. The CNeT contains m-ary nodes and grows during learning by using inheritance to initialize new nodes. At the node level, the CNeT employs unsupervised competitive learning. The CNeT performs hierarchical clustering of the feature vectors presented to it as examples, while its growth is controlled by forward pruning. Because of the tree structure, the prototype in the CNeT close to any example can be determined by searching only a fraction of the tree. This paper introduces different search methods for the CNeT, which are utilized for training as well as for recall. The CNeT is evaluated and compared with existing classifiers on a variety of pattern classification problems.