Fair Streaming Feature Selection

流式特征选择技术已经成为处理实时数据流的必备工具，因为它们有助于从不断更新的信息中识别出最相关的属性。尽管这些算法表现出色，但目前的流式特征选择算法在处理偏见和避免由敏感属性引起的歧视方面经常存在不足，这可能导致所得模型的不公平结果。为了解决这个问题，我们提出了一种新的算法FairSFS，用于公平流式特征选择，以在特征选择过程中维护公平性，同时不影响在线处理数据的能力。FairSFS通过动态调整特征集来适应传入的特征向量，并从这个修订后的集合中区分分类属性和敏感属性之间的相关性，从而防止敏感数据的传播。实证评估表明，FairSFS不仅保持了与领先的流式特征选择方法和现有公平特征技术相当的准确性，而且显著改善了公平性指标。

FairSFS: A Fair Streaming Feature Selection Algorithm

FairSFS is a novel algorithm that ensures fairness in feature selection for real-time data streams by dynamically adjusting the feature set and discerning correlations between sensitive attributes and classification attributes.

FairSFS addresses the issue of bias and discrimination in streaming feature selection, while maintaining accuracy and significantly improving fairness metrics. Empirical evaluations show that FairSFS performs on par with leading methods and existing fair feature techniques. The algorithm is designed to handle data in an online manner and adapt to incoming feature vectors.

Related work includes recent studies on fair feature selection and bias mitigation in machine learning, such as 'Learning Fair Representations for Heterogeneous Face Recognition' and 'Fairness Constraints: Mechanisms for Fair Classification'.