Optimization of Decision Rules Derived from Random Forest

Random forests are powerful classifiers, but their complexity sometimes makes them difficult to interpret. In this paper, a method to extract and optimize decision rules from random forests using two key criteria: rule length (simplicity) and support (coverage), is proposed. Four optimization strategies based on length, support, and their combinations were employed in processing six benchmark datasets. The results show that pruning decision trees before rule extraction leads to shorter and more general rules. Among all strategies, optimizing for support followed by length consistently achieves the best accuracy while maintaining interpretability. This approach offers a practical way to create rule-based models that are both accurate and easier to understand.