To extend the proposed IAE-LSTM-KL model for multi-class anomaly detection scenarios, several modifications and enhancements can be implemented. One approach is to adjust the model's architecture to accommodate multiple classes by incorporating a one-vs-all strategy. This strategy involves training separate models for each class, treating anomalies in each class as the positive class and all other classes as the negative class. By training multiple models in this manner, the model can effectively detect anomalies across multiple classes.
Another method to handle multi-class anomaly detection is to utilize a hierarchical approach. In this approach, the model can be structured in a hierarchical manner, where anomalies are detected at different levels of abstraction or granularity. This hierarchical structure allows for the detection of anomalies at various levels of complexity, providing a more comprehensive approach to multi-class anomaly detection.
Additionally, ensemble methods can be employed to combine the outputs of multiple models trained on different classes. By aggregating the predictions of individual models, the ensemble model can provide a more robust and accurate detection of anomalies across multiple classes.
Overall, extending the IAE-LSTM-KL model for multi-class anomaly detection involves adapting the model's architecture, training strategy, and ensemble techniques to effectively handle anomalies in multiple classes.