51 citations · 300 across the 23 of their papers we have counts for
5 papers · 1 filter
Unsupervised Unlearning of Concept Drift with Autoencoders
André Artelt, Kleanthis Malialis, Christos Panayiotou +2
Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsol…
Data augmentation on-the-fly and active learning in data stream classification
Kleanthis Malialis, Dimitris Papatheodoulou, Stylianos Filippou +2
There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challen…
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
Kleanthis Malialis, Manuel Roveri, Cesare Alippi +2
In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in…
Nonstationary data stream classification with online active learning and siamese neural networks
Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging nee…
Performance Analysis of Event-Triggered Consensus Control for Multi-agent Systems under Cyber-Physical Attacks
Farzaneh Tatari, Aquib Mustafa, Majid Mazouchi +3
This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols. It is sh…