Iterative mixed integer programming model for fuzzy rule-based classification systems

Fuzzy rule based systems have been successfully applied to the pattern classification problem. In this research, we proposed an iterative mixed-integer programming algorithm to generate fuzzy rules for fuzzy rule-based classification systems. The proposed model is capable of assigning the attributes to the antecedents of rules so that their inclusion enhances the accuracy and coverage of that rule. To generate several diverse rules per class, the integer programming model is run iteratively and all samples predicted correctly are temporarily removed from the training dataset in each iteration. This process ensures that subsequent rule covers new samples in the associated class. The proposed model was evaluated on the benchmark datasets from the UCI repository and this comparative study verifies that this approach extracts accurate rules and has advantage over conventional approaches for high dimensional datasets.