The interest in developing machine learning (ML) algorithms for streaming data research has increased over the years. Complex problems provide data with new characteristics in comparison to the previous decade. Nowadays, data sources generate high dimensional information in large volumes and velocity. The generated data are becoming increasingly ubiquitous. To extract useful information is a real challenge. The provided data does not have a homogeneous structure, and it can also be redundant, noisy, and incomplete. These characteristics have motivated the development of several machine learning algorithms for data streams analysis. Streaming data research has mainly focused on developing accurate decision models with the ability to learn and forget concepts incrementally. The advances in streaming data research have been useful in areas such as clustering, temporal learning, anomaly detection, semi-supervised learning, novel class detection, and feature selection. ML research for data streams often appears on the big data (BD) analysis domain, related to tackling issues concerning to velocity and volume of the BD.
Life-Long Learning in Practical Applications http://l3ipa.kssk.pwr.edu.pl/ during IJCNN 2022
Life-Long Learning: Recent Advances and Challenges during IJCNN 2023 https://2023.ijcnn.org/paper-submission/special-sessions
Paper presentation at the IEEE Conference on Big Data 2022, IEEE SMC 2022, ISDA 2022, ACIIDS 2022, ICAISC 2022.
Prof. Michal Wozniak delivered three plenary papers related to the project:
14th Asian Conference on Intelligent Information and Database Systems ACIIDS 2022 https://aciids.pwr.edu.pl/2022/keynotes.php
10th Machine Intelligence and Digital Interaction MIDI Conference https://midi2022.opi.org.pl/program/
6th SLAAI - International Conference on Artificial Intelligence 2022 https://slaai.lk/icai/2022/#speakers