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Auto-Tuning of parameters in hybrid sampling method for class imbalance problem
Author(s)
Sanguanmak, Yotsathon
Date Issued
February 21, 2017
Type
Conference Paper
Abstract
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.
Citation
20th International Computer Science and Engineering Conference Smart Ubiquitos Computing and Knowledge Icsec 2016, 2017