Apr 11, 2023 · Random Undersampling (RUS) is applied to induce five class ratios. The classifiers are evaluated with both the Area Under the Receiver Operating Characteristic Curve (AUC), and Area Under the Precision Recall Curve (AUPRC) metrics. We show that AUPRC provides a better insight into classification performance.

May 27, 2021 · We explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 dataset. This study considers a total of eight random undersampling (RUS) ratios: no sampling, 999:1, 99:1, 95:5, 9:1, 3:1, 65:35, and 1:1.

Oct 1, 2021 · We empirically evaluated UFFDFR by comparing it with three classic undersampling methods (CC, NM, and RUS) and three state-of-the-art clustering-based undersampling methods (KMC, KMN, and CBUS).

Aug 1, 2022 · Given that AUC is a threshold independent measure, can undersampling or oversampling of the majority/minority class during training improve the performance of a binary classifier? In my experience balancing the classes has the same effect as changing the threshold when making predictions.

Based on statistical analysis, the RUS ratio is a significant factor for the AUC metric in detecting all three individual web attacks in the CSE-CIC-IDS2018 dataset for: Brute Force, XSS, and SQL Injection web attacks.

Aug 4, 2023 · Area Under the Curve is a metric used to measure the performance of classification models. AUC represents the area under the ROC (Receiver Operating Characteristic) curve of the...

May 1, 2023 · To cope with this challenge, we proposed RankOptAUC NB (RNB), a novel attribute weighting method for the NB. In the proposed method, learning a weighted NB classifier was formulated as a nonlinear optimization problem with the objective of maximizing the area under the ROC (AUC).

May 20, 2021 · an AUC score of 0.9416 and 1:1 RUS ratio applied. All four of the ensemble learners (LGB, RF , XGB, and CB) have dramatic improvements in AUC scores as increased levels

Results in Fig. 1 show that increasing the size of the majority class beyond 99% does not improve performance beyond that of RUS-1-2. This suggests that both the class imbalance level and the...

This study proposes two resampling methods, namely RUS and SMOTE as one way to balance the data.