Research Article
A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data
Table 4
Text classification performance with different models using 20-Newsgroup dataset.
| ā | Classifier | Noise factor | 0.00 | 0.01 | 0.02 | 0.03 | 0.04 | 0.05 |
| Accuracy | NB | 0.7506 | 0.7274 | 0.6895 | 0.6678 | 0.5887 | 0.4633 | KNN | 0.6136 | 0.6161 | 0.6213 | 0.6142 | 0.6043 | 0.5978 | SVM | 0.7598 | 0.7527 | 0.7294 | 0.6968 | 0.6652 | 0.6453 | DBN | 0.7235 | 0.7207 | 0.7041 | 0.6849 | 0.6562 | 0.6252 | DDNN | 0.7536 | 0.7561 | 0.7550 | 0.7542 | 0.7443 | 0.7378 |
| Recall | NB | 0.7483 | 0.6693 | 0.5053 | 0.3526 | 0.2613 | 0.2027 | KNN | 0.5959 | 0.6000 | 0.6070 | 0.6034 | 0.5939 | 0.5820 | SVM | 0.7525 | 0.7415 | 0.6966 | 0.6094 | 0.4891 | 0.3833 | DBN | 0.7149 | 0.7120 | 0.6990 | 0.6826 | 0.6439 | 0.6250 | DDNN | 0.7459 | 0.7500 | 0.7549 | 0.7534 | 0.7439 | 0.7320 |
| -score | NB | 0.7494 | 0.6971 | 0.5832 | 0.4615 | 0.3619 | 0.2820 | KNN | 0.6046 | 0.6079 | 0.6141 | 0.6088 | 0.5991 | 0.5898 | SVM | 0.7561 | 0.7471 | 0.7126 | 0.6502 | 0.5637 | 0.4809 | DBN | 0.7192 | 0.7163 | 0.7015 | 0.6837 | 0.6500 | 0.6251 | DDNN | 0.7497 | 0.7530 | 0.7549 | 0.7538 | 0.7441 | 0.7349 |
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