A Distributed Hierarchical Graph Neuron-Based Classifier: An Efficient, Low-Computational Classifier

Many of the widely used classifiers are time consuming and resource intensive, and hence not practical to be used in the emerging wireless networks. We present an efficient classifier, termed distributed hierarchical graph neuron (DHGN)-based classifier. Our proposed solution uses a new form of neural network, which consists of a hierarchical graph-based representation of input patterns, and adopts a one-cycle learning process. We compare the effectiveness and computational complexity of our proposed classifier with the well known self-organizing map (SOM) classifier in a supervised environment. The results show that the DHGN-based classifier offers lower computational complexity than SOM while guaranteeing satisfactory classification accuracy.