@proceedings {ma2020isolation,
	title = {Isolation Mondrian Forest for Batch and Online Anomaly Detection},
	journal = {IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020},
	year = {2020},
	abstract = {<p>
	We propose a new method, named isolation Mon- drian 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.
</p>
},
	url = {arXiv preprint arXiv:2003.03692},
	author = {Ma, Haoran and Ghojogh, Benyamin and Samad, Maria N and Zheng, Dongyu and Crowley, Mark}
}
