Using discretization to improve E-commerce anomaly detection process

Effective data mining solutions have been anticipated in Electronic Commerce (E-Commerce) transaction anomaly detection model to accurately predict anomaly transaction records. However, there are many sub-optimal E-Commerce transaction anomaly detection models due to highly imbalanced data set. This research paper proposes a preprocessing method based discretization of continuous variables to solve the problem of highly imbalanced data. The Logistic Regression, Naive Bayes, RBFNetwork and NBtree classifiers are applied to evaluate the discretization method. Results indicate that the discretization method can achieve excellent performance.