to be evenly distributed. However, in the imbalanced classification, the training data set of one majority class could far surpass those of the minority class. This becomes a problem because it’s usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over minority class. One popular method recently used to rectify this is the SMOTE (Synthetic Minority Over-Sampling Technique) which combines algorithms at data level. Therefore, this paper presents a novel approach for learning and imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure by focusing on a two-class problem. The Bidikmisi data set is imbalanced, because the distribution of majority class examples is 15 times the number of minority class examples. All models have been evaluated using stratified 5-fold cross-validation, and the performance criteria (such as Recall, F-Value and G-Mean) are examined. The results show that the SMOTE-Boosting algorithms have a better classification performance than the AdaBoost.M2 method, as the g-mean value increases 4-fold after the SMOTE method is used. We can say that SMOTE-Boosting algorithm is quite successful when taking advantage of boosting algorithms with SMOTE. When boosting affects the accuracy of the random forest by focusing on all data classes, the SMOTE algorithm alters the performance values of the random forest only in minority classes.
- Imbalanced classification