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Komplexa inbyggda system i realtid

Cyber-Physical Systems Analysis

Tillförlitlig programvaruteknik

Heterogena system

Lärande och optimering

Säkerhetskritisk teknik

Simulation and optimisation for future industrial applications (SOFIA)

Ubiquitous Computing

Mälardalen Interaction and Didactics (MIND)

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

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. 

Project manager at MDH

Professor

Ning Xiong

+4621151716

ning.xiong@mdh.se

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.