Integrating Instance Selection and Bagging Ensemble using a Genetic Algorithm

Ensemble classification combines individually trained classifiers to obtain more accurate predictions than individual classifiers alone. Ensemble techniques are very useful for improving the generalizability of the classifier. Bagging is the method used most commonly for constructing ensemble classifiers. In bagging, different training data subsets are drawn randomly with replacement from the original training dataset. Base classifiers are trained on these different bootstrap samples. Instance selection is used to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Although instance selection and bagging have proven effective in many data mining applications, few studies have considered the integration of the two. This study proposes a new method to integrate instance selection and bagging ensemble using genetic algorithms to improve the performance of the model. A genetic algorithm is used to select optimal or near-optimal instances to be used as input data by the bagging model. This study applies the proposed model to a bankruptcy-prediction problem. The experimental results show that the proposed model outperformed the other models.