Congratulations to our labmates and collaborators: Diandra Prioleau, Kiana Alikhademi, Armisha Roberts, Joshua Peeples, Alina Zare and Juan Gilbert!  Their paper, “Application of Divisive Clustering for Reducing Bias in Imbalanced Data” was recently accepted to the the 2021 International Conference on Machine Learning and Data Mining (MLDM).

In their paper, the authors propose the use of Applications Quest (AQ) as an under-sampling technique to combat the challenge of non-diverse and non-representative training data. 

Check out the paper here!