标题： 使用混合Transformer-自编码器框架改进实时概念漂移检测 显示英文标题

标题： Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework

摘要： 在应用机器学习中，概念漂移（即数据分布的渐进或突然变化）可能会显著降低模型性能。 典型的检测方法，如统计检验或基于重构的模型，通常是被动的，并且对早期检测不够敏感。 我们的研究提出了一种由Transformer和自编码器组成的混合框架，以建模复杂的时序动态并提供在线漂移检测。 我们创建了一种独特的信任得分方法，该方法包括（1）统计和基于重构的漂移指标，更具体地说，是PSI、JSD、Transformer-AE误差，（2）预测不确定性，（3）规则违反情况，以及（4）与信任得分定义的组合指标相一致的分类器误差趋势。 使用带有合成漂移的时间序列航空乘客数据集，我们的模型在整体上以及在不同检测阈值下，相比基线方法，在灵敏度和可解释性方面都能更好地检测漂移，并为实际机器学习中的实时漂移检测提供了一个强大的流程。 我们使用了一个时间序列的航空乘客数据集，其中逐步注入了期望的漂移刺激，例如后期批次中排列的机票价格，分为10个时间段[1]。在数据中，我们的结果支持Transformer-自编码器比文献中常用的自编码器更早且更敏感地检测漂移，并在更高的错误率和逻辑违规情况下提供了改进的建模。 因此，开发了一个稳健的框架，以可靠地监控概念漂移。 显示英文摘要

摘要： In applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typical detection methods,such as statistical tests or reconstruction-based models,are generally reactive and not very sensitive to early detection. Our study proposes a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics and provide online drift detection. We create a distinct Trust Score methodology, which includes signals on (1) statistical and reconstruction-based drift metrics, more specifically, PSI, JSD, Transformer-AE error, (2) prediction uncertainty, (3) rules violations, and (4) trend of classifier error aligned with the combined metrics defined by the Trust Score. Using a time sequenced airline passenger data set with synthetic drift, our proposed model allows for a better detection of drift using as a whole and at different detection thresholds for both sensitivity and interpretability compared to baseline methods and provides a strong pipeline for drift detection in real time for applied machine learning. We evaluated performance using a time-sequenced airline passenger dataset having the gradually injected stimulus of drift in expectations,e.g. permuted ticket prices in later batches, broken into 10 time segments [1].In the data, our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature, and provided improved modeling over more error rates and logical violations. Therefore, a robust framework was developed to reliably monitor concept drift.