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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Rank-Based Support Vector Machines for Highly Imbalanced Data Using Nominated Samples

Mohammad Jafari Jozani, University of Manitoba, Winnipeg, Canada

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