An efficient structure learning algorithm for a self-organizing neuro-fuzzy multilayered classifier

In authors' previous works, a novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) was proposed. SONeFMUC is composed of small-scale interconnected fuzzy neuron classifiers (FNCs) arranged in layers. The structure of the classifier is revealed by means of the well known GMDH algorithm. In addition, the GMDH algorithm inherently implements feature selection, considering the most informative attributes as model inputs. However, previous simulation results indicate that the GMDH algorithm calculates a large number of FNCs with slightly higher or even the same classification capabilities than its parents. Hence, the computational cost of the GMDH is large without a direct impact to the classification accuracy. In this paper, a modified version of GMDH is proposed for an effective identification of the structure of SONeFMUC with reduced computational cost. To this end, a statistical measure of agreement of the generic FNCs in classifying the patterns of the problem is used. This measure is known as Proportion of Specific Agreement (Ps). Hence, only complementary FNCs are combined to construct a descendant FNC at the next layer and the total number of constructed FNCs is reduced. The proposed structure learning algorithm is tested on a well known classification problem of the literature, the forensic glass. Simulation results indicate the efficiency of the proposed algorithm.