Differential Evolution-Based Weighted Voting Stacking Ensemble Classifier for Highly Skewed Binary Data Distribution

In an era where cybercrime is getting more and more complex by day, the accuracy in classifying credit card transactions is an important issue. This chapter argues if the weighted voting stacking ensemble method that combines various classifier models can be utilized as solution to this issue. But in using such solution, selecting the appropriate weights of classifier models for the correct classification of credit card transactions is a problem, which can be viewed as a problem of weights optimization. This chapter proposes to use the differential evolution optimization method as approach to define the appropriate weight function for the weight voting stacking ensemble method of various classification methods. It is found that the number of true positive transactions generated by the stacking ensemble method is exceeded (0.7% vs 85.4%) by that of the true positives generated using the differential evolution optimization.