ADAPTER: Adaptive Learning and Information Fusion for Online Classification Based on Evolving Big Data Streams

Research Group:


Status:

active

Start date:

2017-01-01

End date:

2020-12-31

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. 



[Show all publications]

MapReduce distributed highly random fuzzy forest for noisy big data (Jul 2017)
Faruk Mustafic , Ning Xiong, Francisco Herrera, Sergio Ramrez
13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 (ICNC-FSKD-2017)

Big data stream learning based hybridized Kalman filter and backpropagation through time (Jul 2017)
He Fan, Ning Xiong
13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 (ICNC-FSKD-2017)

Alopex-Based Mutation Strategy in Differential Evolution (Jun 2017)
Miguel Leon Ortiz, Ning Xiong
IEEE Congress on Evolutionary Computation 2017 (IEEE CEC 2017)

Adaptive Differential Evolution Supports Automatic Model Calibration in Furnace Optimized Control System (Jan 2017)
Miguel Leon Ortiz, Magnus Evestedt , Ning Xiong
Computational Intelligence (CI)


Ning Xiong, Professor

Room: U1-126
Phone: +46-21-151716