Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification
International Journal of artificial intelligence research
View Publication Info| Field | Value | |
| Title |
Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification
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| Creator |
Hayaty, Mardhiya
Muthmainah, Siti Ghufran, Syed Muhammad |
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| Subject |
—
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| Description |
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%.
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| Publisher |
STMIK Dharma Wacana
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| Contributor |
—
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| Date |
2021-01-05
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
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| Format |
application/pdf
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| Identifier |
http://ijair.id/index.php/ijair/article/view/152
10.29099/ijair.v4i2.152 |
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| Source |
International Journal of Artificial Intelligence Research; Vol 4, No 2 (2020): December; 86 - 94
2579-7298 10.29099/ijair.v4i2 |
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| Language |
eng
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| Relation |
http://ijair.id/index.php/ijair/article/view/152/pdf
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| Rights |
Copyright (c) 2021 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0 |
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