A Multi-Granularity Broad Fuzzy Apriori Classifier Using Confidence-Weighted Learning

Abstract

This study proposes a novel multi-granularity broad fuzzy Apriori classifier (MGB-FAC) that addresses the high computational complexity and low generalization capability of FAC while sharing FAC’s high linguistic interpretability and strong uncertainty-handling ability. The basic idea of MGB-FAC is as follows: First, MGB-FAC creates its FAC sub-classifiers using improved feature subsets and randomly discards some rules for each sub-classifier. Second, MGB-FAC splits the rule sets of each sub-classifier into several new sub-classifiers with different rule granularity according to the length of the rules. Finally, MGB-FAC aggregates the outputs of all sub-classifiers using confidence-weighted learning to obtain the final output. MGB-FAC has three clear advantages: (1) It achieves a highly reduced computational burden while preserving the acceptable learning ability of each FAC sub-classifier. (2) It constructs many multi-granularity FAC sub-classifiers with greater diversity while requiring only a small additional computational cost without increasing the number of fuzzy rules. (3) It realizes a good broad ensemble by weighting the contribution of each FAC sub-classifier based on its confidence value and performance on input data, in terms of accuracy, precision, recall, and F1 score. The effectiveness of the proposed MGB-FAC is verified by extensive experiments using ten benchmarking datasets compared with seven comparative methods.