Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approac...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | EJournal Article |
| Published: |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2020-01-31.
|
| Subjects: | |
| Online Access: | Get Fulltext Get Fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02335 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_37415 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Ustyannie, Windyaning |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Suprapto, Suprapto |e author |
| 245 | 0 | 0 | |a Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2020-01-31. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/37415 | ||
| 520 | |a The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset. | ||
| 540 | |a Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Imbalanced Datasets; RWO-Sampling; Logistic Regression | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 1 (2020): January; 1-10 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/37415/26952 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/37415 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/37415/26952 |z Get Fulltext |