An Improvement in Classification Accuracy of Fuzzy Oriented Classifier Evolution

Fuzzy ORiented Classifier Evolution (FORCE) is a graph-based genetic fuzzy system which we have previously proposed. FORCE constructs fuzzy classification rules automatically by evolving directed graphs composed of fuzzy conditions using Genetic Algorithm. In this work, to improve FORCE about efficiency of rule optimization and expressiveness of Membership Functions (MFs), we introduce two new ideas into FORCE: Edge Mutation (EM) and Parameter tunable MFs (PMFs). EM changes node connections with considering current graph structure to develop rules efficiently unlike the original mutation changing them just randomly. PMFs are MFs characterized by real-coded parameters optimized using uniform and non-uniform mutation. PMFs improve the expressiveness of MFs, which are represented by combination of user-defined parameters in the previous work. We tested the improved FORCE with 21 classification data sets in comparison with our previous model and a common method, and experimental results showed the proposed ideas improved classification accuracy of FORCE.