The aim of the project is to develop a new methodology for adaptive, distributed learning and information fusion from evolving data streams, based on the MapReduce paradigm. For the Map function, we will investigate adaptive learning methods of updating fuzzy approximate rules to assimilate new events and/or concept changes, given nonstationary and imbalanced data streams. For the Reduce function, we will develop an instance-based learning mechanism to reach more accurate results in the final decision about classification.
|First Name||Last Name||Title|
|Miguel||Leon Ortiz||Senior Lecturer|
Federated fuzzy learning with imbalanced data (Dec 2021) Lukas Dust, Marina López Murcia, Petter Nordin , Andreas Mäkilä, Ning Xiong, Francisco Herrera IEEE Conference on Machine Learning and Applications 2021 (ICMLA'21)
BELIEF: A distance-based redundancy-proof feature selection method for Big Data (Feb 2021) D. López, Sergio Ramírez-Gallego, Salvador García, Ning Xiong, Francisco Herrera Information Sciences (INS21)
An Incremental fuzzy learning approach for online classification of data streams (Dec 2020) Vladyslav Yavtukhovskyi, Rami Abukhader, Nils Tillaeus, Ning Xiong 12th International Conference on Soft Computing and Pattern Recognition 2020 (SoCPaR2020)
Intelligent traffic signal control based on reinforcement learning (Dec 2020) Natalija Dokic, Miroljub Tomic, Jasmina Stevic, Dunja Dokic, Ning Xiong International Conference on Intelligent Systems Design and Applications (ISDA2020)
Smart Case Mining based on Membrane Clustering (Dec 2019) Johan Holmberg, Ning Xiong IEEE Symposium Series on Computational Intelligence 2019 (ISSCI19)