[jira] [Updated] (MADLIB-990) SVM - novelty detection using 1-class SVM

["Frank McQuillan (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MADLIB-990?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frank McQuillan updated MADLIB-990:
-----------------------------------
    Fix Version/s: v1.9.1

> SVM - novelty detection using 1-class SVM
> -----------------------------------------
>
>                 Key: MADLIB-990
>                 URL: https://issues.apache.org/jira/browse/MADLIB-990
>             Project: Apache MADlib
>          Issue Type: New Feature
>            Reporter: Frank McQuillan
>             Fix For: v1.9.1
>
>
> Story
> As a data scientist, I want to use  a one-class SVM so that I can decide whether a new observation belongs to the same distribution as existing observations (an inlier), or should be considered as different (an outlier). 
> Acceptance
> 1) One-class SVM implemented with all supported kernel types (linear, gaussian, polynomial).
> 2) Output a T/F for not-novel/novel.
> Note
> a) Similar e1071 R package [1] with
> type=one-classification (for novelty detection)
> b) There is an important distinction between novelty detection (this story) and outlier detection for cleaning training data.  From reference [2]:
> * novelty detection:  the training data is not polluted by outliers, and we are interested in detecting anomalies in new observations. <- this story
> * outlier detection:  the training data contains outliers, and we need to fit the central mode of the training data, ignoring the deviant observations. <- we are *not* trying to solve this unsupervised learning problem in this story.
> References
> [1] e1071 R package
> https://cran.r-project.org/web/packages/e1071/index.html
> [2] Difference between novelty and outlier detection
> http://scikit-learn.org/stable/modules/outlier_detection.html



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