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Using feature construction for dimensionality reduction in big data scenarios to allow real time classification of sequence data

  • A sequence of transactions represents a complex and multi dimensional type of data. Feature construction can be used to reduce the data´s dimensionality to find behavioural patterns within such sequences. The patterns can be expressed using the blue prints of the constructed relevant features. These blue prints can then be used for real time classification on other sequences.

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Metadaten
Name:Laux, Friedrich
URN:urn:nbn:de:bsz:rt2-opus4-6154
URL:http://subs.emis.de/LNI/Proceedings/Proceedings244/article7.html
ISBN:978-3-88579-638-1
Erschienen in:Digital enterprise computing (DEC 2015)
Publisher:Gesellschaft für Informatik
Place of publication:Bonn
Editor:Alfred Zimmermann, Alexander Rossmann
Document Type:Conference Proceeding
Language:English
Year of Publication:2015
Tag:big data; feature construction; real time classification
Pagenumber:11
First Page:259
Last Page:269
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:004 Informatik
Open Access:Ja
Licence (German):License Logo  Open Access