Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature

Abstract

Fuzzy interpolation improves the applicability of fuzzy inference by allowing the utilisation of sparse rule bases. Curvature-based rule base generation approach has been recently proposed to support fuzzy interpolation. Despite the ability to directly generating sparse rule bases from data, the approach often suffers from the high dimensionality of complex inference problems. In this work, a different curvature calculation approach, i.e., the Menger approach, is employed to the curvature-based rule base generation approach in an effort to address the limitation. The experimental results confirm better efficiency and efficacy of the proposed method in generating rule bases on high-dimensional datasets.