FairGLVQ: Fairness in Partition-Based Classification

1 code implementation • 16 Oct 2024 • Felix Störck, Fabian Hinder, Johannes Brinkrolf, Benjamin Paassen, Valerie Vaquet, Barbara Hammer

The contribution of this work is twofold: 1) we develop a general framework for fair machine learning of partition-based models that does not depend on a specific fairness definition, and 2) we derive a fair version of learning vector quantization (LVQ) as a specific instantiation.

Classification Fairness +2