ENSEMBLE LEARNING DENGAN METODE SMOTEBAGGING PADA KLASIFIKASI DATA TIDAK SEIMBANG

Rimbun Siringoringo, Indra Kelana Jaya

Abstract


Unbalanced data classification is a crucial problem in the field of machine learning and data mining. Data imbalances have a poor impact on classification results where minority classes are often misclassified as a majority class. Conventional machine learning algorithms are not equipped with the ability to work on unbalanced data, so the performance of conventional algorithms is always not optimal. In this study, ensemble learning using SMOTEBagging method was applied to classify 11 unbalanced datasets. SMOTEBagging performance is also compared with three types of conventional classification algorithms namely SVM, k-NN, and C4.5. By applying the 5 cross-validation scheme, the AUC value generated by SMOTEBagging is higher at 10 datasets. The mean values of the lowest to highest AUC were obtained by SVM, k-NN, C4.5 and SMOTEBagging algorithms with values 0.638, 0.742, 0.770 and 0.895. By applying Friedman test it was found that the performance of AUC SMOTEBagging differed significantly with the other three conventional methods SVM, k-NN and C4.5

ENSEMBLE LEARNING DENGAN  METODE  SMOTEBagging PADA KLASIFIKASI DATA TIDAK SEIMBANG


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Department of Information System| Computer Science Faculty | Universitas Pelita Harapan | sistech.medan@uph.edu