Discovering Accurate and Interesting Classification Rules Using Genetic Algorithm

Discovering accurate and interesting classification rules is a significant task in the post-processing stage of a data mining (DM) process. Therefore, an optimization problem exists between the accuracy and the interesting metrics for post-processing rule sets. To achieve a balance, in this paper, we propose two major post-processing tasks. In the first task, we use a genetic algorithm (GA) to find the best combination of rules that maximizes the predictive accuracy on the sample training set. Thus we obtain the maximized accuracy. In the second task, we rank the rules by assigning objective rule interestingness (RI) measures (or weights) for the rules in the rule set. Henceforth, we propose a pruning strategy using a GA to find the best combination of interesting rules with the maximized (or greater) accuracy. We tested our implementation on three data sets. The results are very encouraging; they demonstrate the applicability and effectiveness of our approach.