Abstract
Using fuzzy rule interpolation (FRI) interpolative reasoning can be effectively performed with a sparse rule base where a given system observation does not match any fuzzy rules. While offering a potentially powerful inference mechanism, in the current literature, typical representation of fuzzy rules in FRI assumes that all attributes in the rules are of equal significance in deriving the consequents. This is a strong assumption in practical applications, thereby, often leading to less accurate interpolated results. To address this challenging problem, this paper employs feature selection (FS) techniques to adjudge the relative significance of individual attributes and therefore, to differentiate the contributions of the rule antecedents and their impact upon FRI. This is feasible because FS provides a readily adaptable mechanism for evaluating and ranking attributes, being capable of selecting more informative features. Without requiring any acquisition of real observations, based on the originally given sparse rule base, the individual scores are computed using a set of training samples that are artificially created from the rule base through an innovative reverse engineering procedure. The attribute scores are integrated within the popular scale and move transformation-based FRI algorithm (while other FRI approaches may be similarly extended following the same idea), forming a novel method for attribute ranking-supported fuzzy interpolative reasoning. The efficacy and robustness of the proposed approach is verified through systematic experimental examinations in comparison with the original FRI technique over a range of benchmark classification problems while utilizing different FS methods. A specific and important outcome that is supported by attribute ranking, only two (i.e., the least number of) nearest adjacent rules are required to perform accurate interpolative reasoning, avoiding the need of searching for and computing with multiple rules beyond the immediate neighborhood of a given observation.
Highlights
F UZZY rule interpolation (FRI) enables fuzzy rule-based reasoning systems to perform inference with a sparse rule base [1], [2]
We present the relevant background work, including an outline of fuzzy rule interpolation (FRI) based on scale and move transformations and a brief description of selected feature selection (FS) methods to be used for attribute ranking
This paper presents a range of choices regarding the weighting methods that may be utilized to support and refine fuzzy interpolative reasoning, as described in the following
Summary
F UZZY rule interpolation (FRI) enables fuzzy rule-based reasoning systems to perform inference with a sparse rule base [1], [2]. For any reasoning system (be it fuzzy or boolean), different ranking scores of features or domain attributes imply different contributions of them to the inference outcome Inspired by this observation, a novel weighted FRI approach is proposed here, consolidating upon the initial ideas presented in [38], where a feature evaluation method is integrated within the FRI procedure to score the significance of individual rule antecedents. A novel weighted FRI approach is proposed here, consolidating upon the initial ideas presented in [38], where a feature evaluation method is integrated within the FRI procedure to score the significance of individual rule antecedents This is different from existing techniques for rule interpolation that involve weights (e.g., [39]–[41]), which construct an interpolated result by weighted aggregation of rule consequents, where rule importances are ranked using Euclidean distance between rule antecedents and a given observation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
