Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ Article Swipe

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vector quantization computer science quantization (signal processing) learning vector quantization artificial intelligence support vector machine algorithm

Marika Kaden , Alexander Engelsberger , Ronny Schubert , Sofie Lövdal , Elina van den Brandhof , Michael Biehl , Thomas Villmann ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.14428/esann/2025.es2025-65 · OA: W4409479066

We propose a bias detection and mitigating scheme for data in the context of classification tasks based on learning vector quantizers (LVQ) as classifier. For this purpose generalized LVQ endowed with an advanced matrix adaptation scheme is used for bias detection. The bias removal from data is realized applying a nullspace data projection using the adjusted matrix. The usefulness of the approach is demonstrated and illustrated in terms of two real world datasets.

Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ Article Swipe

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