Ensemble Method combine Naive Bayes and Euclidean Distance for Classification Binary Imbalanced Data

With the development of Data Mining, ensemble methods have been widely applied to classify binary imbalanced data. Traditional ensemble rules, such as Max rule, Min rule, Product rule, and Sum rule have been proved could not meet the needs of classification of binary imbalanced data. So this paper proposed an ensemble rule which take Naive Bayes as base classifier and the Euclidean distance between the new data and train data and relations of majority classes and minority classes are taken into account in the new ensemble rule. The reason is that it can strengthen the relationship between the classify results and raw data. Simulation results are provided to confirm that the proposed method has better performance than existing ensemble methods while dealing with binary imbalanced data in the performance of Area Under roc Curve(AUC). So, the proposed method in this paper has a good performance while dealing with binary imbalanced data.