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Artificiell intelligens och intelligenta system

Automatiserade mjukvaruspråkutveckling och mjukvaruteknik

Digitalisering

Formell modellering och analys av inbyggda system

Förnybar energi

Industriell programvaruteknik

Komplexa inbyggda system i realtid

Lärande och optimering

Medicinsk teknik

Modellbaserad konstruktion av inbäddade system

Programmeringsspråk

Certifierbara bevis och justifieringsteknik

Cyber-fysisk systemanalys

Datakommunikation

Heterogena system

Programvarutestlaboratorium

Resurseffektivisering

Säkerhetskritisk teknik

Teknisk matematik

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.

Avslutat

Start

2017-01-01

Avslut

2020-12-31

Huvudfinansiering

Forskningsområde

Forskningsinriktning

Projektansvarig vid MDH

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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.

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