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

TL;DR

This paper introduces a hybrid Transformer-Autoencoder framework with a novel Trust Score for early, sensitive, and interpretable real-time concept drift detection in machine learning, outperforming baseline methods.

Contribution

The study presents a new hybrid Transformer-Autoencoder model combined with a Trust Score methodology for improved real-time concept drift detection.

Findings

Transformer-Autoencoder detects drift earlier than standard autoencoders.

The proposed method improves sensitivity and interpretability in drift detection.

The framework outperforms baseline methods on airline passenger data with synthetic drift.

Abstract

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.