论文标题
隔离蒙德里安森林用于批处理和在线异常检测
Isolation Mondrian Forest for Batch and Online Anomaly Detection
论文作者
论文摘要
我们提出了一种新方法，称为孤立森林（Imondrian Forest），用于批处理和在线异常检测。所提出的方法是一种新型的隔离森林和蒙德里安森林的混合物，分别是批处理异常检测和在线随机森林的现有方法。 Imondrian Forest使用树中的节点深度进行隔离的想法，并将其在蒙德里亚森林结构中实现。结果是一种新的数据结构，可以在用于异常检测的同时以在线方式接受流数据。我们的实验表明，在批处理设置中，Imondrian森林的性能比隔离森林的表现更好，并且与其他批处理和在线异常检测方法具有更好或可比的性能。
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.
