End-to-End Hierarchical Fuzzy Inference Solution

Hierarchical Fuzzy System (HFS) is a popular approach for handling curse of dimensionality problem occurred in complex fuzzy rule-based systems with various and numerous inputs. However, the processes of modeling and reasoning of HFS have some critical issues to be considered. In this study, the effect of these issues on the accuracy and stability of the resulting system has been investigated, and an end-to-end HFS framework has been proposed. The proposed framework has three main steps such as single system modeling, rule partitioning and HFS reasoning. It is fully automated, generic, almost independent from data, and applicable for any kind of inference problem. In addition, the proposed framework preserves accuracy and stability during the HFS reasoning. These judgments have been ensured by a number of experimental studies on several datasets about software faulty prediction (SFP) problem with a large feature space. The main contributions of this paper are as follows: (i) it provides the entire HFS implementation from problem definition to calculation of final output, (ii) it increases the accuracy of recently proposed rule generation scheme in the literature, (iii) it presents the only possible fuzzy system solution for SFP problem containing a large feature space with reasonable accuracy.