1/20/2022: Automatic Binary Classification, Alexis Espinoza, Diego Henríquez and Catalina Pezo, University of Concepcion
In this presentation, the speakers showed their preliminary work on Automatic ML Algorithm Selection for Binary Classification. For this, they showed how they collected and generated a number of binary classification (92 public and 4000 self-generated) instances (datasets) on which the task was to predict the correct label for the unlabeled entries, based on the labeled entries whose percentage, in each dataset, could vary. They ran 9 different classification algorithms with default parameters and trained a deep learning model as in , achieving a 54% accuracy on the testing dataset. Several follow-up ideas about how to generate adequate training data and characterize the instances were discussed.
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