[cosc-grad-students-list] FW: Final iCORE workshop for Spring 2026 - Evan Krell - Imbalanced Data Strategies

[King, Scott]

CS community,

We have the final iCORE workshop of Spring 2026 this week,  today at 4:00pm, iCORE (NRC2100) and online. See attached calendar invitation.

As always, we will have iCORE updates at 3:30, and the workshop will start at 4pm, and you are welcome to attend either or both.  Both are in NRC 2100, but you can join virtually as well.


Machine Learning Workshop: Imbalanced Data Strategies

Dr. Evan Krell

Naval Research Lab - Marine Meteorology Division


A major challenge in machine learning is dealing with imbalanced datasets. That is, where the dataset does not have an equal number of samples from each class. In some situations, the dataset can be very imbalanced because of the actual rarity of certain events, so class balance can not be obtained through additional sampling. For example, to train a model for predicting hurricanes there are very few hurricane examples compared to a massive amount of non-hurricane examples. This makes it difficult to model extreme events. To deal with this, several class balancing methods have been proposed that have become very popular: Random Undersampling, Random Oversampling, SMOTE, and weighted loss functions. While they can be effective at improving skill at predicting the minority class, they have some major disadvantages. Mainly, the output probability loses any statistical meaning. In this workshop, I show that these data balancing methods might not do what you think they are doing and that there is another, simpler, alternative for tuning models under class imbalance that is applied after training the model so that the original probabilities that represent the training distribution are maintained.




Scott King
Professor of Computer Science
Director, iCORE
Texas A&M University – Corpus Christi


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