Tag: undersampling
DIVISIVE CLUSTERING ACCEPTED TO MLDM!
March 19, 2021Congratulations 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 […]
Read more: DIVISIVE CLUSTERING ACCEPTED TO MLDM! »APPLICATION OF DIVISIVE CLUSTERING FOR REDUCING BIAS IN IMBALANCED DATA
March 19, 2021Abstract: A lack of diversity and representativeness within training data causes bias in the machine learning pipeline by influencing the performance of many machine learning models to favor the majority of samples that are most similar. It is necessary to have diverse and representative training data, especially for application domains in which people of varying […]
Read more: APPLICATION OF DIVISIVE CLUSTERING FOR REDUCING BIAS IN IMBALANCED DATA »