[jira] [Resolved] (SPARK-16098) Multiclass SVM Learning

["Sean Owen (JIRA)" <ji...@apache.org>]

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

Sean Owen resolved SPARK-16098.
-------------------------------
    Resolution: Won't Fix

> 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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