A comparative study of optimization models in genetic programming-based rule extraction problems

In this manuscript, we identify and evaluate some of the most used optimization models for rule extraction using genetic programming-based algorithms. Six different models, which combine the most common fitness functions, were tested. These functions employ well-known metrics such as support, confidence, sensitivity, specificity, and accuracy. The models were then applied in the assessment of the performance of a single algorithm in several real classification problems. Results were compared using two different criteria: accuracy and sensitivity/specificity. This comparison, which was supported by statistical analysis, pointed out that the use of the product of sensitivity and specificity provides a more realistic estimation of classifier performance. It was also shown that the accuracy metric can make the classifier biased, especially in unbalanced databases.