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All Journal International Journal of Artificial Intelligence Research
Ghufran, Syed Muhammad
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Computer Science & IT Electrical & Electronics Engineering

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Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification Hayaty, Mardhiya; Muthmainah, Siti; Ghufran, Syed Muhammad
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.603 KB) | DOI: 10.29099/ijair.v4i2.152

Abstract

High accuracy value is one of the parameters of the success of classification in predicting classes. The higher the value, the more correct the class prediction.  One way to improve accuracy is dataset has a balanced class composition. It is complicated to ensure the dataset has a stable class, especially in rare cases. This study used a blood donor dataset; the classification process predicts donors are feasible and not feasible; in this case, the reward ratio is quite high. This work aims to increase the number of minority class data randomly and synthetically so that the amount of data in both classes is balanced. The application of SOS and ROS succeeded in increasing the accuracy of inappropriate class recognition from 12% to 100% in the KNN algorithm. In contrast, the naïve Bayes algorithm did not experience an increase before and after the balancing process, which was 89%. 
Co-Authors Hayaty, Mardhiya Siti Mutmainah