5 papers · 1 filter
Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling
Jin Li, Kleanthis Malialis, Christos G. Panayiotou +1
In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are unlabeled, making it challengin…
Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams
Jin Li, Kleanthis Malialis, Marios Polycarpou
In today's connected world, the generation of massive streaming data across diverse domains has become commonplace. In the presence of concept drift, class imbalance, label scarcit…
Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
Jin Li, Kleanthis Malialis, Stelios G. Vrachimis +1
Water Distribution Networks (WDNs), critical to public well-being and economic stability, face challenges such as pipe blockages and background leakages, exacerbated by operational…
Online Detection of Water Contamination Under Concept Drift
Jin Li, Kleanthis Malialis, Stelios G. Vrachimis +1
Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine,…
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Kleanthis Malialis, Jin Li, Christos G. Panayiotou +1
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, conc…