178 citations · 369 across the 22 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…
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
Dimitris Papatheodoulou, Pavlos Pavlou, Stelios G. Vrachimis +3
Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we re…