Fair Feature Selection: A Causal Perspective

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-02-03 DOI:10.1145/3643890

Zhaolong Ling, Enqi Xu, Peng Zhou, Liang Du, Kui Yu, Xindong Wu

引用次数: 0

Abstract

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a Fair Causal Feature Selection algorithm, called FairCFS. Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has comparable accuracy compared to eight state-of-the-art feature selection algorithms, while presenting more superior fairness.

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公平特征选择：因果视角

用于分类决策任务的公平特征选择最近引起了研究人员的极大关注。然而，现有的公平特征选择算法无法全面解释特征与敏感属性之间的因果关系，这可能会影响公平特征识别的准确性。为了解决这个问题，我们提出了一种公平因果特征选择算法，称为 FairCFS。具体来说，FairCFS 构建了一个本地化因果图，用于识别类和敏感变量的马尔可夫空白，从而阻断敏感信息的传递，以选择公平的因果特征。在七个公开的真实世界数据集上进行的大量实验验证了 FairCFS 与八种最先进的特征选择算法相比，具有相当的准确性，同时具有更优越的公平性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

6.70

自引率

5.60%

发文量

172

审稿时长

3 months

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