A Novel Fuzzy Rule Based Neuro-system with Sparse Rule Extraction for Classification Problems

The generation of fuzzy rule base and rule extraction are efficient approaches to enhance the performance of fuzzy rule system. Here, we propose a novel fuzzy neural network structure that can be used for rule extraction. First of all, inspired by the compactly combined fuzzy rule base (CoCo-FRB) and fully combined fuzzy rule base (FuCo-FRB), we develop a new fuzzy rule base, compromise fuzzy rule base (CmPm-FRB), which generates rules by cutting off the long rules and compensating the short ones. In addition, Group Lasso penalty is utilized in the objective function with the rule threshold to produce sparsity in rules in a grouped manner for rule extraction. However, as the Group Lasso penalty is not differentiable at the origin, a tiny bias term is added to the gradient formula to achieve the smoothness. In order to verify the effectiveness of the proposed model, extensive experiments are conducted on ten classification data sets. The empirical results explicitly demonstrate the effectiveness of the proposed model for classification problems.