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    Auto-Tuning of parameters in hybrid sampling method for class imbalance problem
    (2017-02-21)
    Sanguanmak, Yotsathon
    ;
    Hanskunatai, Anantaporn
    The class imbalance is a major problem in machine learning. This problem affects the performance of a model prediction. The DBSM algorithm, a hybrid-sampling technique, was developed to deal with the class imbalance for two-class classification problem. Although the DBSM algorithm is the effective solution, there are too many parameters for tuning in the algorithm. Thus, this paper proposes an automatic parameter tuning for the DBSM algorithm by using a genetic algorithm (GA), called GADBSM. The experimental results of GADBSM are compared with the DBSM algorithm. The results show that the GADBSM can enhance the classification performance of the DBSM algorithm. Moreover, the GADBSM provides the best in F-measure and AUC in all datasets.
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    DBSM: The combination of DBSCAN and SMOTE for imbalanced data classification
    (2016-11-18)
    Sanguanmak, Yotsathon
    ;
    Hanskunatai, Anantaporn
    Many applications in the real world encounter the class imbalance problem. This problem affects the performance of the model prediction. Nowadays, resampling technique is a popular technique to handle the class imbalance problem such as oversampling, undersampling, and hybridsampling. Thus, this paper proposes a new hybrid resampling technique to deal with the class imbalance problem, called DBSM. The concept of DBSM is to use DBSCAN algorithm for undersampling and apply SMOTE technique for oversampling. The experimental results of the DBSM algorithm are compared with an original datasets and other sampling techniques, which are SMOTE, Tomek Links, SMOTE+Tomek Links and DBSCAN. The results show that the DBSM can improve the predictive performance of the classifiers. In addition, it yields the best in the average of AUC, F-measure, and accuracy.