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

["Hayri Volkan Agun (JIRA)" <ji...@apache.org>]

Hayri Volkan Agun created SPARK-16098:
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             Summary: Multiclass SVM Learning
                 Key: SPARK-16098
                 URL: https://issues.apache.org/jira/browse/SPARK-16098
             Project: Spark
          Issue Type: Request
          Components: ML, MLlib
    Affects Versions: 2.0.0, 2.1.0
         Environment: Spark MLLib and ML 1.6.1
            Reporter: Hayri Volkan Agun
             Fix For: 2.1.0


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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