Feature ranking-guided fuzzy rule interpolation
Fangyi Li, Changjing Shang, Ying Li, Qiang Shen · 2017
Fuzzy rule interpolation (FRI) provides an alternative means to make inference with a sparse rule base, rather than directly resulting in failed reasoning when no rules can be fired for an input observation. However, existing approaches to FRI typically assume that rule antecedents are of equal significance in the implementation of interpolation, thereby often leading to less accurate interpolated results. Having taken notice of feature selection (FS) techniques being capable of selecting (subsets of) informative features, providing a mechanism of evaluating and ranking features, this work employs FS to score the individual rule antecedents in a given rule base. In particular, the computation of individual scores is enabled by the introduction of an innovative reverse engineering technique that artificially creates a set of training samples from a given sparse rule base. The antecedent scores are integrated within the scale and move transformation-based FRI algorithm (though other FRI approaches may employ the same idea), forming a novel feature ranking-guided FRI method. The work is systematically examined, by utilising six different FS techniques and comparing over eight benchmark classification problems, demonstrating improved classification performance.