Data augmentation on-the-fly and active learning in data stream classification
K Malialis, D Papatheodoulou… - 2022 IEEE …, 2022 - ieeexplore.ieee.org
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 challenge …
machine learning applications data are arriving in an online fashion. A critical challenge …
Autoencoder-based anomaly detection in streaming data with incremental learning and concept drift adaptation
J Li, K Malialis, MM Polycarpou - 2023 International Joint …, 2023 - ieeexplore.ieee.org
In our digital universe nowadays, enormous amount of data are produced in a streaming
manner in a variety of application areas. These data are often unlabelled. In this case …
manner in a variety of application areas. These data are often unlabelled. In this case …
Unsupervised unlearning of concept drift with autoencoders
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 …
samples. Consequently, learning models operating on the data stream might become …
A hybridization of multiple imputation and one-class bagging ensemble approach for missing value and class imbalance problem
P Baro, MD Borah - Evolving Systems, 2024 - Springer
Class imbalance in a dataset leads to erroneous outcomes that engrave the learning
techniques and high misclassification cost in the minority class. Along with class imbalance …
techniques and high misclassification cost in the minority class. Along with class imbalance …
Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences
J Li, K Malialis, CG Panayiotou… - … Joint Conference on …, 2024 - ieeexplore.ieee.org
In the contemporary digital landscape, the continuous generation of extensive streaming
data across diverse domains has become pervasive. Yet, a significant portion of this data …
data across diverse domains has become pervasive. Yet, a significant portion of this data …
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
K Malialis, J Li, CG Panayiotou… - arXiv preprint arXiv …, 2024 - arxiv.org
Data stream mining aims at extracting meaningful knowledge from continually evolving data
streams, addressing the challenges posed by nonstationary environments, particularly …
streams, addressing the challenges posed by nonstationary environments, particularly …