## 6 Experimental Results
### Performance of Baseline Drift Adaptation Models for Anomaly Classification  
As discussed in Section 4.3, we analyze the effectiveness of our adaptive and optimized anomaly classification model of TWIN-ADAPT by comparing it with seven baseline models for drift adaptation. Four of these models--including ARF-ADWIN, ARF-DDM, SRP-ADWIN and SRP-DDM--are customized by combining drift detectors (ADWIN and DDM) with drift adaptors (ARF and SRP) to create robust models capable of handling concept drift. The other three individual models for online drift adaptation are EFDT, LB and HB. The performances of the various baseline drift adaptation models on the testing datasets, following their training on the respective training sets, are depicted in Figures 16-18. For the Fumehood dataset, as shown in Figure 16, the SRP-DDM model achieves the highest average accuracy of 97.01%, followed by the LB model at 96.45% and the SRP-ADWIN model at 95.53%. The ARF-ADWIN and ARF-DDM models also maintain high accuracy levels, whereas the EFDT and HT models perform significantly worse, with average accuracies of 86.11% and 91.62%, respectively. On the Vacuum Pump dataset (Figure 17), the SRP-DDM model again shows a superior performance with an average accuracy of 67.33%, closely followed by the LB model at 67.93%. The SRP-ADWIN and ARF-DDM models also maintain competitive accuracy levels. However, the EFDT and HT models lag behind, with average accuracies of 62.53% and 65.41%, respectively. For the Lithography dataset (Figure 18), the results indicate that the SRP-DDM model leads with an average accuracy of 64.82%, closely matched by the ARF-DDM and SRP-ADWIN models. The ARF-ADWIN model also performs relatively well, while the EFDT model shows a lower accuracy at 55.27%. The HT model achieves an average accuracy of 45.56%, indicating that it struggles the most with this dataset. To complement these results, Table 4 also presents the F1 scores, precision, recall and accuracy for various models across the Fumehood, Vacuum Pump, and Lithography Machine datasets. In observing the F1 scores and precision values, the SRP-DDM model consistently achieves a high performance across all three datasets. For instance, the SRP-DDM model has an F1 score of 0.97 for the Fumehood dataset, 0.705 for the Vacuum Pump dataset and 0.678 for the Lithography Machine dataset, indicating its superior performance in comparison to the other baseline drift adaptation models. It is worth noting that across  
Figure 15: Static ML models’ performances on training and testing datasets for Lithography Unit. all datasets, the models initially struggle to adapt but achieve stable performances after processing a few samples, demonstrating their ability to handle concept drifts effectively over time. This trend is evident in the initial fluctuations followed by stabilization in the accuracy metrics. In summary, while the SRP-DDM and LB models generally exhibit the highest performances across the datasets, the other baseline drift adaptation learners such as ARF-ADWIN, ARF-DDM and SRP-ADWIN also maintain competitive accuracies. Conversely, the EFDT and HT models consistently show low performances as they fail to capture the intricate relationships within the data due to the models' reliance on simpler, rule-based structures for adaptation.