FairGLVQ: Fairness in Partition-Based Classification

TL;DR

This paper introduces a flexible framework for ensuring fairness in partition-based machine learning models and presents a fair version of learning vector quantization, demonstrating its effectiveness on various datasets.

Contribution

Develops a general, fairness-agnostic framework for partition-based models and derives a novel fair learning vector quantization algorithm.

Findings

The fair LVQ outperforms existing algorithms on theoretical data.

The approach is effective on real-world datasets.

The framework is adaptable to different fairness definitions.

Abstract

Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence. Due to the increasing importance of machine learning approaches in all areas of daily life including those related to health, security, and equity, an increasing amount of research focuses on fair machine learning. In this work, we focus on the fairness of partition- and prototype-based models. 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. We compare the resulting algorithm against other algorithms from the literature on theoretical and real-world data showing its practical relevance.