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.

本文引入一种新颖的方法，即面向公平性自适应记忆分类器的进化多目标优化，旨在增强机器学习算法在数据流分类中的公平性。该方法通过将自适应记忆 K 最近邻算法的优势与进化多目标优化相结合，有效地处理流数据中的概念漂移，并同时最大化准确性和最小化歧视。通过在各种数据集上进行广泛实验，将提出的方法与几种基准方法在准确性和公平度指标方面进行比较，证明了该方法的有效性。结果表明，该方法具有竞争力的准确性并显著减少了歧视，突显其作为面向公平性数据流分类的稳健解决方案的潜力。进一步的分析也证实了触发进化多目标优化和适应分类器的策略在提出的方法中的有效性。

演化多目标优化在数据流中用于公平感知的自适应内存分类器

Evolutionary Multi-Objective Optimisation for Fairness-Aware Self  Adjusting Memory Classifiers in Data Streams

Data stream classification is an important problem in the field of machine
learning. Due to the non-stationary nature of the data where the underlying
distribution changes over time (concept drift), the model needs to continuously
adapt to new data statistics. Stream-based Active Learning (AL) approaches
address this problem by interactively querying a human expert to provide new
data labels for the most recent samples, within a limited budget. Existing AL
strategies assume that labels are immediately available, while in a real-world
scenario the expert requires time to provide a queried label (verification
latency), and by the time the requested labels arrive they may not be relevant
anymore. In this article, we investigate the influence of finite,
time-variable, and unknown verification delay, in the presence of concept drift
on AL approaches. We propose PRopagate (PR), a latency independent utility
estimator which also predicts the requested, but not yet known, labels.
Furthermore, we propose a drift-dependent dynamic budget strategy, which uses a
variable distribution of the labelling budget over time, after a detected
drift. Thorough experimental evaluation, with both synthetic and real-world
non-stationary datasets, and different settings of verification latency and
budget are conducted and analyzed. We empirically show that the proposed method
consistently outperforms the state-of-the-art. Additionally, we demonstrate
that with variable budget allocation in time, it is possible to boost the
performance of AL strategies, without increasing the overall labeling budget.

本文研究了有关时间变量和未知延迟验证对于主动学习问题的影响，提出了一种基于 PRopagate 和一个依据漂移情况的动态预算策略的解决方案。通过实验评估，发现该方法在不同的延迟时间和预算设置下优于现有技术。此外，还证明了在时间上变化预算分配可以提高主动学习策略的性能，而不增加总体标记预算。

非平稳环境下基于数据流的具有验证延迟的主动学习

Stream-based Active Learning with Verification Latency in Non-stationary Environments

The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the $\mbox{Tornado}$ framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
$\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art.

该研究提出 Tornado 框架，它实现了一些不同学习风格的分类器和各种漂移检测算法，用于构建模型以针对不断变化的数据流，评估结果表明，最佳分类器与检测器选择不仅取决于数据流的特征而且会随着流的变化而演变。