Research Article
Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets
Pseudocode 3
The third set of experiments (noise injection).
(1) | Load the training portion of the intrusion dataset | (2) | Convert the label values from numeric to nominal (only for UNSW-NB15) | (3) | Conduct noise injection by manipulating the labels of the training instances (5, 10, 20, and 30%) | (4) | Train the ML algorithm on the noisy training dataset | (5) | Run the ML algorithm on the testing dataset | (6) | Document the results | (7) | Revert to step 3 until each of the ML algorithms is trained and tested with the different levels of noise |
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