Drift Detection
Drift detection focuses on identifying changes in the statistical properties of data streams over time, a phenomenon known as concept drift, impacting the performance of machine learning models. Current research emphasizes developing unsupervised methods, particularly those leveraging deep learning representations (like autoencoders) and generative adversarial networks (GANs), to detect both global and localized drifts, as well as addressing the challenges posed by high-dimensional data and limited labeled data. Effective drift detection is crucial for maintaining the accuracy and reliability of machine learning systems across diverse applications, from wildlife monitoring and manufacturing to fraud detection and personalized recommendations, enabling timely model adaptation and improved system performance.
Papers
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
Jin Li, Kleanthis Malialis, Marios M. Polycarpou
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks
Firas Bayram, Phil Aupke, Bestoun S. Ahmed, Andreas Kassler, Andreas Theocharis, Jonas Forsman
Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis
Elias Werner, Nishant Kumar, Matthias Lieber, Sunna Torge, Stefan Gumhold, Wolfgang E. Nagel
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Congcong Liu, Fei Teng, Xiwei Zhao, Zhangang Lin, Jinghe Hu, Jingping Shao
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo, James Pope, Marcello Bullo, Ingram Weeks, Vijay Kumar, Pietro Carnelli, George Oikonomou, Theodoros Spyridopoulos, Aftab Khan
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
Ioannis Mavromatis, Aftab Khan