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https://issues.apache.org/jira/browse/SPARK-22867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16301072#comment-16301072
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Fangzhou Yang commented on SPARK-22867:
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Currently, there is no special algorithm for novelty and outlier detection in
MLlib. Isolation Forest is a very popular and effective algorithm for outlier
detection, which is also included in scikit-learn. Therefore, I think it might
be good and practical if it can be provided in Spark MLlib.
> Add Isolation Forest algorithm to MLlib
> ---------------------------------------
>
> Key: SPARK-22867
> URL: https://issues.apache.org/jira/browse/SPARK-22867
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.2.1
> Reporter: Fangzhou Yang
>
> Isolation Forest (iForest) is an effective model that focuses on anomaly
> isolation.
> iForest uses tree structure for modeling data, iTree isolates anomalies
> closer to the root of the tree as compared to normal points.
> A anomaly score is calculated by iForest model to measure the abnormality of
> the data instances. The lower, the more abnormal.
> More details about iForest can be found in the following papers:
> <a href="https://dl.acm.org/citation.cfm?id=1511387";>Isolation Forest</a> [1]
> and <a href="https://dl.acm.org/citation.cfm?id=2133363";>Isolation-Based
> Anomaly Detection</a> [2].
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