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The performance of detection model using the NCC-1 dataset is presented in this Table. The model with the Decision Tree algorithm shows the highest performance with an accuracy of 99.97%, precision of 99.89%, and recall of 99.89% in the model without feature extraction. The highest performance of 99.98% accuracy, 99.96% precision, and 99.89% recall on the Random Forest algorithm is also obtained from the model without feature selection. In the model that implements the Naïve Bayes algorithm, the highest accuracy performance of 88.13% and 68.19% precision is obtained from the model that implements feature selection with Factor Analysis. The highest performance in the detection model using the k-NN algorithm is obtained in the model that applies feature selection PCA and Fast ICA with an accuracy of 99.60%, precision with a PCA of 98.72%, and recall with a Fast ICA of 98.42. The model with the Logistic Regression algorithm has the highest accuracy performance on Fast ICA and Factor Analysis at 86.02% and the highest recall of 52.17% on PCA. |
IEEE/ICACT20230225 Slide.21
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