Improving genetic classifiers with a boosting algorithm

We present a boosting genetic algorithm for classification rule discovery. The method is based on the iterative rule learning approach to genetic classifiers. The boosting mechanism increases the weight of those training instances that are not classified correctly by the new rules, so that in the next iteration the algorithm focuses the search on those rules that capture the misclassified or uncovered instances. We show that the boosted genetic classifier has higher accuracy for prediction, or from an alternative and perhaps more important perspective, uses less computational resources for similar accuracy, than the original genetic classifier.