# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Model Performance Evaluation
Notably, although LSTM-VAE and STAD report F1-scores (0.62 and 0.57) comparable to ADDAEIL (0.61), their AUC values (0.81 and 0.76) are significantly lower than ADDAEIL's (0.85). F1 reflects performance at a specific threshold, while AUC captures overall distinction across thresholds. LSTM-VAE relies on a static model that cannot maintain stable decision boundaries when data evolves, which leads to fluctuations in the ROC curve. STAD uses a discrete state switching and retraining mechanism that blurs decision boundaries, resulting in a lower AUC. In contrast, ADDAEIL achieves a higher AUC through continuous local updates and a combined structure-performance evaluation, maintaining robust distinction between normal and anomalous data. Compared to AEADF, ADDAEIL gives more accurate and focused updates. AEADF compares outputs to a fixed base model, which becomes outdated over time. In contrast, ADDAEIL uses structure-based performance evaluation to identify which detectors are most affected by drift. It replaces only the components that require updating. As a result, it improves precision (0.58 vs. 0.48) while keeping high recall. These results show that ADDAEIL is effective in handling diverse and unpredictable drift in real-world data streams. **CEMACS Dataset:** The CEMACS dataset, derived from ocean surface temperature measurements, exhibits relatively mild incremental drift driven primarily by seasonal variations. With a window size of \(w=48\), each window covers approximately two days, during which the short-term temperature changes are limited. As a result, the degree of drift between adjacent windows remains subtle, presenting a relatively stable streaming environment. Under this setting, the performance differences across methods are less pronounced compared to datasets with stronger drift patterns. Most models achieve reasonably high AUC and F1-scores, indicating that even static or semi-adaptive methods can maintain acceptable performance in slowly evolving conditions. The two deep learning-based algorithms, LSTM-VAE and STAD, also demonstrate competitive capability in this scenario. However, ADDAEIL continues to outperform all the state-of-the-art methods, achievingthe highest F1-score of 0.90 and maintaining both high recall and precision. This demonstrates that even in low-drift scenarios, ADDAEIL's design enables it to preserve long-term accuracy through its effective and incremental adaptation strategy. Unlike approaches that require full model retraining, ADDAEIL introduces minimal updates guided by statistical signals, allowing it to incorporate new data gradually while retaining useful historical knowledge. This balance between adaptability and stability is particularly important in real-world applications such as environmental monitoring, where drift is continuous but subtle. These results further validate the effectiveness of ADDAEIL's detection and update mechanism in handling relatively stationary real-world scenarios. **Overall:** Further analysis highlights the benefits of ADDAEIL across all three datasets. On both Yahoo A3 and NAB, which exhibit significant concept drift, the results show that ADDAEIL is able to achieve the highest recall while also improving precision and F1-score. This demonstrates that ADDAEIL can accurately track drifting data patterns and selectively update model components, which helps avoid underfitting (i.e., conservative updates when drift is present) as well as overreaction (i.e., aggressive updates when drift is absent). On CEMACS, where the degree of drift is relatively mild and gradual, ADDAEIL still achieves top performance without incurring excessive model changes. The average results of all baselines over all datasets are reported in the last row in Table 2 and summarized in Figure 4. As expected, ADDAEIL obtains the highest F1-score of 0.76 and outperforms the next best baseline AEADF, which achieves 0.67. ADDAEIL also reaches the highest average precision of 0.75 and the highest AUC of 0.90.