Isolation Mondrian Forest for Batch and Online Anomaly Detection
This work addresses anomaly detection for data streams, but it is incremental as it combines existing methods.
The authors tackled the problem of anomaly detection in both batch and streaming data by proposing iMondrian forest, a hybrid of isolation forest and Mondrian forest, which showed improved performance over isolation forest in batch settings and was competitive with other methods.
We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods.