A hybrid fuzzy feature selection algorithm for high-dimensional regression problems: An mRMR-based framework

One of the most important factors affecting the interpretability of Fuzzy Rule-Based Systems (FRBSs) is the number of features used Indeed, the employment of a large number of features could be problematic for the components of an FRBS. In these cases,feature selection approaches can be employed to discover the most beneficial features. In this study, we present HFFS a Hybrid Fuzzy Feature Selection algorithm for high-dimensional regression problems. It benefits from both filter and wrapper methods. HFFS is composed of two main components: a filter-based Selector and a wrapper-based Modifier. Selector is an mRMR-based framework that sequentially selects the most informative features and adds them to a candidate subset. However, since these features may not result in an efficient FRBS, they are evaluated by Modifier, which fine-tunes them whenever their overall performance declines. This procedure is repeated until no further progress is possible within the candidate subset, acting as the stopping point for the algorithm. The effectiveness of HFFS was evaluated using twenty-eight real-world regression datasets and four different types of FRBSs. The experimental results and statistical tests confirmed that HFFS was able to improve the estimation accuracy compared to the filter methods and reduce the computation times compared to the wrapper methods.