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.
| Author of HS Reutlingen | 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 ZimmermannORCiD, Alexander RossmannORCiD |
| Document Type: | Conference proceeding |
| Language: | English |
| Publication year: | 2015 |
| Tag: | big data; feature construction; real time classification |
| Page Number: | 11 |
| First Page: | 259 |
| Last Page: | 269 |
| PPN: | Im Katalog der Hochschule Reutlingen ansehen |
| DDC classes: | 004 Informatik |
| Open access?: | Ja |
| Licence (German): |  Open Access |
