21

Jun

Teaser image to Rank-based support vector machines for highly imbalanced data using nominated samples

Rank-based support vector machines for highly imbalanced data using nominated samples

Mohammad Jafari Jozani, University of Manitoba, Winnipeg, Canada

   21.06.2023

   4:15 pm - 5:45 pm

   LMU Department of Statistics and via zoom

The talk proposes a novel approach, MaxNS, that tackles highly imbalanced binary classification using expert opinions and rank information. Biasing training samples towards the minority class, it employs rank-based Hinge and Logistic loss functions.


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