Using Ensemble Classifiers to Detect Incipient Anomalies
This work addresses a critical challenge in anomaly detection for domains like industrial systems, where early detection can prevent failures, but it is incremental as it builds on existing ensemble methods.
The paper tackled the problem of detecting incipient anomalies, which are hard to identify due to their similarity to normal conditions and lack of training examples, by using ensemble learning to leverage uncertainty information, resulting in improved performance as demonstrated through experiments on two real-world datasets.
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. The lack of incipient anomaly examples in the training data can pose severe risks to anomaly detection methods that are built upon Machine Learning (ML) techniques, because these anomalies can be easily mistaken as normal operating conditions. To address this challenge, we propose to utilize the uncertainty information available from ensemble learning to identify potential misclassified incipient anomalies. We show in this paper that ensemble learning methods can give improved performance on incipient anomalies and identify common pitfalls in these models through extensive experiments on two real-world datasets. Then, we discuss how to design more effective ensemble models for detecting incipient anomalies.