Research Article
Software Defect Prediction through Neural Network and Feature Selections
Table 15
Comparison of RBF results with other previous methods before feature selection for PC1 data set.
| | Source | Algorithm | F-measure | Accuracy |
| | [43] | J star | N/A | 87.61% |
| | [47] | Naive bayes | 0.11 | 88.07% | | MLP | 0.11 | 30.09% | | SVM | 0.07 | 93.09% | | RBF | 0.12 | 93.13% |
| | [31] | RF | 0.91 | 91.00% | | DS | 0.88 | 87.00% | | SVM | 0.83 | 79.00% | | LR | 0.85 | 81.00% |
| | [49] | RBF | 0.154 | 94.60% | | KNN | 0.286 | 92.64% | | DT | 0.500 | 93.13% |
| | This research | RBF | 0.99 | 98.99 |
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