Rule Acquisition with an Entropy-Based Hybrid Genetic Algorithm

The paper describes the implementation and the functioning of RAHGA (Rule Acquisition with an Entropy-based Genetic Algorithm), a genetic-algorithm-based data mining system suitable for both supervised and certain types of unsupervised knowledge extraction from large and possibly noisy databases. RAHGA differs from a standard Genetic Algorithm. We compared our approach with several other traditional data mining techniques. The results show that our approach outperformed others on both the prediction accuracy and the standard deviation.