Collective of Base Classifiers for Mining Imbalanced Data

Mining imbalanced datasets is a challenging and difficult problem. In this paper we adress it by proposing GEP-NB classifier based on the oversampling technique. It combines two learning methods - Gene Expression Programming and Naive Bayes, which cooperate to produce a final prediction. At the pre-processing stage a simple mechanism for generating synthetic minority class examples and balancing the training set is used. Next, two genes g1 and g2 are evolved using Gene Expression Programming. They differ by applying in each case a different procedure for selecting synthetic minority class examples. If the class prediction by g1 agrees with the class prediction made by g2, their decision is final. Otherwise the final predictive decision is taken by the Naive Bayes classifier. The approach is validated in an extensive computational experiment. Results produced by GEP-NB are compared with performance of several state-of-the-art classifiers. Comparisons show that GEP-NB offers a competitive performance.