The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.
Evaluating and improving anomaly detection algorithms. The latest source code of **iForest** and **iNNE** can be obtained from [**here**](https://github.com/zhuye88/iNNE). The source code of IDK used for group anomaly detection and time series anomaly detection can be obtained from [**here**](https://github.com/IsolationKernel/Codes/tree/main/IDK).
With a different isolation mechanism, iNNE has been shown to outperform iForest in terms of detecting local anomalies and tolerance to irrelevant attributes, which becomes obvious in the high‐dimensional data sets.