Research Article
AWSMOTE: An SVM-Based Adaptive Weighted SMOTE for Class-Imbalance Learning
Table 5
Accuracy with different oversampling methods from real datasets.
| Id | NO-RS | SMOTE | BLSMOTE | ADASYN | DBSMOTE | AWSMOTE |
| 1 | 0.9691 | 0.9747 | 0.9626 | 0.9628 | 0.9668 | 0.9767 | 2 | 0.8962 | 0.9243 | 0.8911 | 0.9129 | 0.9135 | 0.9261 | 3 | 0.9162 | 0.9904 | —— | 0.9953 | 0.9949 | 0.9973 | 4 | 0.9571 | 0.9648 | —— | 0.9626 | 0.9709 | 0.9748 | 5 | 0.8489 | 0.9154 | 0.9371 | 0.9536 | 0.9164 | 0.9515 | 6 | 0.8985 | 0.9247 | 0.9215 | 0.9229 | 0.9016 | 0.9125 | 7 | 0.9133 | 0.9929 | 0.9855 | 0.9940 | 0.9877 | 0.9938 | 8 | 0.7212 | 0.8434 | 0.8814 | 0.9027 | 0.8494 | 0.9030 | 9 | 0.8942 | 0.9357 | 0.9385 | 0.9318 | 0.8900 | 0.9549 | 10 | 0.9640 | 0.9542 | 0.9731 | 0.9729 | 0.9768 | 0.9774 | 11 | 0.9220 | 0.9553 | 0.9543 | 0.9525 | 0.9490 | 0.9559 | 12 | 0.9602 | 0.9933 | —— | 0.9837 | 0.9933 | 0.9943 | 13 | 0.9334 | 0.9862 | —— | 0.9859 | 0.9867 | 0.9874 | 14 | 0.9534 | 0.9960 | —— | 0.9952 | 0.9897 | 0.9978 | 15 | 0.9274 | 0.9765 | —— | 0.9993 | 0.9988 | 1 | 16 | 0.9169 | 0.9939 | —— | 0.9937 | 0.9939 | 0.9942 | 17 | 0.9118 | 0.9987 | —— | 0.9985 | 0.9891 | 1 | 18 | 0.9808 | 0.9878 | —— | 0.9926 | 0.9822 | 0.9935 | 19 | 0.9760 | 0.9798 | —— | 0.9905 | 0.9878 | 0.9944 | 20 | 0.9523 | —— | —— | —— | 0.9919 | 0.9960 | 21 | 0.9777 | 0.9795 | —— | 0.9777 | 0.9905 | 0.9876 | 22 | 0.9428 | 0.9927 | —— | 0.9943 | 0.9932 | 0.9946 |
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