A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers

In this article, an efficient structure learning algorithm is proposed for the development of self-organizing neuro-fuzzy multilayered classifiers (SONeFMUC). These classifiers are hierarchical structures comprising small-scale fuzzy-neuron classifiers (FNCs), interconnected along multiple layers. At each layer, parent FNCs are combined to construct a descendant FNC at the next layer with enhanced classification qualities. The SONeFMUC structure is progressively expanded by generating new layers based on the principles of the Group Method of Data Handling (GMDH) algorithm, which is appropriately adapted to handle classification tasks. Traditional GMDH proceeds blindly to the construction of all possible parent FNC pairs from the previous layer to obtain the individuals in the next layer without paying due attention to the diversity of the FNC combinations. However, previous experimentation shows that a large number of descendant FNCs exhibit similar or slightly better classification capabilities than their parent FNCs. This causes an increase of the computational cost required for structure learning, without a direct impact on the accuracy of the obtained models. In this paper, a modified version of GMDH is devised for effective identification of the SONeFMUC structure. We incorporate the Proportion of Specific Agreement (Ps) as a means to evaluate the diversity of the FNC pairs. In the devised method, only complementary FNCs are combined, i.e., FNCs which commit errors at different pattern subspaces, to construct a descendant FNC at the next layer. Accordingly, a computational reduction is achieved while high classification accuracy is maintained. The efficiency of the proposed structure learning is tested on a diverse set of benchmark datasets using land cover classification from multispectral images as a real-world application.