A hierarchical approach to multi-class fuzzy classifiers

In this paper we present a hierarchical approach for generating fuzzy rules directly from data in a simple and effective way. The fuzzy classifier results from the union of fuzzy systems, employing the Wang and Mendel algorithm, built on input regions increasingly smaller, according to a multi-level grid-like partition. Key parameters of the proposed method are optimized by means of a genetic algorithm. Only the necessary partitions are built, in order to guarantee high interpretability and to avoid the explosion of the number of rules as the hierarchical level increases. We apply our method to real-world data collected from a photovoltaic (PV) installation so as to linguistically describe how the temperature of the PV panel and the irradiation relate to the class (low, medium, high) of the energy produced by the panel. The obtained mean and maximum classification percentages on 30 repetitions of the experiment are 97.38% and 97.91%, respectively. We also apply our method to the classification of some well-known benchmark datasets and show how the achieved results compare favourably with those obtained by other authors using different techniques.

► We generate the classifier fuzzy rules directly from data in a simple and effective way.
► Through a data-driven hierarchical process, we effectively model the input domain space, avoiding the generation of irrelevant rules.
► The final classifier is the union of fuzzy systems modeling input regions increasingly smaller, based on multi-level grid partitioning.
► We linguistically describe the relation between irradiation and temperature of a PV panel, and energy produced by this panel.
► The proposed approach outperforms other methods found in the literature on some benchmark datasets.