Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Malialis, Kleanthis"'
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution
Externí odkaz:
http://arxiv.org/abs/2404.02572
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 remains unlabeled, posing a challenge in identifying infrequent events s
Externí odkaz:
http://arxiv.org/abs/2403.03576
Autor:
Karapitta, Marianna, Kasis, Andreas, Stylianides, Charithea, Malialis, Kleanthis, Kolios, Panayiotis
The emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and loss of life. The rapid spread of pandemics have made governments across the world adopt a range of actions, including non-phar
Externí odkaz:
http://arxiv.org/abs/2401.06629
Publikováno v:
International Conference on Artificial Neural Networks (ICANN), 2023
Severe acute respiratory disease SARS-CoV-2 has had a found impact on public health systems and healthcare emergency response especially with respect to making decisions on the most effective measures to be taken at any given time. As demonstrated th
Externí odkaz:
http://arxiv.org/abs/2309.09698
Publikováno v:
2023 International Joint Conference on Neural Networks (IJCNN)
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, identifying infrequent events, such as anomalies, poses a great challenge.
Externí odkaz:
http://arxiv.org/abs/2305.08977
Autor:
Artelt, André, Malialis, Kleanthis, Panayiotou, Christos, Polycarpou, Marios, Hammer, Barbara
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 obsolete, and need costly and difficult adjustments such as retraining or a
Externí odkaz:
http://arxiv.org/abs/2211.12989
Autor:
Malialis, Kleanthis, Papatheodoulou, Dimitris, Filippou, Stylianos, Panayiotou, Christos G., Polycarpou, Marios M.
Publikováno v:
IEEE Symposium Series on Computational Intelligence (SSCI), 2022
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 encountered is that of limited availability of ground truth informa
Externí odkaz:
http://arxiv.org/abs/2210.06873
Autor:
Malialis, Kleanthis, Roveri, Manuel, Alippi, Cesare, Panayiotou, Christos G., Polycarpou, Marios M.
Publikováno v:
IEEE Symposium Series on Computational Intelligence (SSCI), 2022
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 the users' behaviour). These changes, often called concept drift, mig
Externí odkaz:
http://arxiv.org/abs/2210.04949
Publikováno v:
Neurocomputing, Volume 512, Pages 235-252, 2022
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 need for online learning methods that train predictive models on-the-fly.
Externí odkaz:
http://arxiv.org/abs/2210.01090
Autor:
Papatheodoulou, Dimitris, Pavlou, Pavlos, Vrachimis, Stelios G., Malialis, Kleanthis, Eliades, Demetrios G., Theocharides, Theocharis
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 refer to as aggregated time series classification. We consider an aggreg
Externí odkaz:
http://arxiv.org/abs/2210.00089