Data and code underlying the publication: DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning

DOI:10.4121/8648064e-aa7b-4a09-a755-7eb2d90bef66.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/8648064e-aa7b-4a09-a755-7eb2d90bef66
Datacite citation style:
Tepeli, Yasin; Gonçalves, Joana (2024): Data and code underlying the publication: DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/8648064e-aa7b-4a09-a755-7eb2d90bef66.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This repository consists of Data/Code to reproduce the results of the thesis chapter "DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning".

The data is shared at: https://doi.org/10.6084/m9.figshare.27003601

The code is shared at: https://github.com/joanagoncalveslab/DCAST

History

  • 2024-11-25 first online, published, posted

Publisher

4TU.ResearchData

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Delft Bioinformatics La