Exploiting decision trees in product-based fuzzy neural modeling to generate rules with dynamically reduced dimensionality

Decision trees are commonly employed as data classifiers in various research fields, but also in real-world application domains. In the fuzzy neural framework, decision trees can offer valuable assistance in determining a proper initial system structure, which means not only feature selection, but also rule extraction and organization. This paper proposes a synergistic model that combines the advantages of a subsethood-product neural fuzzy inference system and a CART algorithm, in order to create a novel architecture and generate fuzzy rules of the form "IF - THEN IF", where the first "IF" concerns the primary attributes and the second "IF" the secondary attributes of the given dataset as defined by our method. The resulted structure eliminates certain drawbacks of both techniques and produces a compact, comprehensible and efficient rulebase. Experiments in benchmark classification tasks prove that this method does not only reduce computational cost, but it also maintains performance at high levels, offering fast and accurate processing during real-time operations.