Abstract
This paper introduces a novel approach, evolutionary multi-objective optimisation for fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine learning algorithms applied to data stream classification. With the growing concern over discrimination in algorithmic decision-making, particularly in dynamic data stream environments, there is a need for methods that ensure fair treatment of individuals across sensitive attributes like race or gender. The proposed approach addresses this challenge by integrating the strengths of the self-adjusting memory K-Nearest-Neighbour algorithm with evolutionary multi-objective optimisation. This combination allows the new approach to efficiently manage concept drift in streaming data and leverage the flexibility of evolutionary multi-objective optimisation to maximise accuracy and minimise discrimination simultaneously. We demonstrate the effectiveness of the proposed approach through extensive experiments on various datasets, comparing its performance against several baseline methods in terms of accuracy and fairness metrics. Our results show that the proposed approach maintains competitive accuracy and significantly reduces discrimination, highlighting its potential as a robust solution for fairness-aware data stream classification. Further analyses also confirm the effectiveness of the strategies to trigger evolutionary multi-objective optimisation and adapt classifiers in the proposed approach.
Abstract (translated)
本文提出了一种新方法,称为进化多目标优化公平感知自调整记忆分类器,旨在提高应用于数据流分类的机器学习算法中的公平性。随着对算法决策中歧视的担忧不断增加,特别是在动态数据流环境中,需要方法来确保对敏感属性(如种族或性别)的公平对待。所提出的方法通过将自调整记忆K-最近邻算法的优势与进化多目标优化相结合来解决这一挑战。这种结合允许新的方法有效地管理数据流中的概念漂移,并利用进化的多目标优化的灵活性来同时最大化准确性和最小化歧视。我们通过在各种数据集上进行广泛的实验,比较了所提出方法在准确性和公平度指标上的表现与多个基线方法的性能。我们的结果表明,与基线方法相比,所提出方法保持了竞争性的准确性和显著减少了歧视,表明其可能成为公平感知数据流分类的稳健解决方案。进一步的分析还证实了策略触发进化多目标优化和自调整分类器在所提出方法中的有效性。
URL
https://arxiv.org/abs/2404.12076
