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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/06/21 12:49:57 UTC
[jira] [Updated] (SPARK-16098) Multiclass SVM Learning

     [ https://issues.apache.org/jira/browse/SPARK-16098?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-16098:
------------------------------
                Flags:   (was: Patch)
    Affects Version/s:     (was: 2.1.0)
     Target Version/s:   (was: 2.1.0)
               Labels:   (was: features patch)
             Priority: Minor  (was: Major)
        Fix Version/s:     (was: 2.1.0)
           Issue Type: Improvement  (was: Request)

Before opening a JIRA, please read https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark Many of the values here didn't make sense; it can't affect a non-existent version for instance.

I understand what you're saying, but, it's generally true that one-vs-all could create imbalanced data sets, though not in all cases. SVMs don't necessarily do badly in unbalanced cases, but could. What specifically are you proposing?

> Multiclass SVM Learning
> -----------------------
>
>                 Key: SPARK-16098
>                 URL: https://issues.apache.org/jira/browse/SPARK-16098
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 2.0.0
>         Environment: Spark MLLib and ML 1.6.1
>            Reporter: Hayri Volkan Agun
>            Priority: Minor
>   Original Estimate: 1,512h
>  Remaining Estimate: 1,512h
>
> There exists a OneVsRest classifier for using all binary classification classifiers in multi-class classification. However for Linear SVM using OneVsRest may create an imbalanced dataset scenarios where SVM of Spark certainly fails. I verified this by creating LinearSVM classifier and implemented predictRaw method of ClassificationModel class. In all experiments the results came very poor in terms of F-Measure. The only explanation is SVM is very sensitive to imbalanced dataset, and naturally OneVsRest classifier creates an imbalanced dataset. 
> For multi-class classification, linear SVM can be optimized by considering imbalanced datasets.      



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