Approaching Concept Drift by Context Feature Partitioning

Approaching Concept Drift by Context Feature Partitioning

In this paper we present a new approach to handle concept drift using domain-specific knowledge. More precisely, we capitalize known context features to partition a domain into subdomains featuring static class distributions. Subsequently, we learn separate classifiers for each sub domain and classi...

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Bibliographic Details
Main Authors: Hoffmann, Nico, Kirmse, Matthias, Petersohn, Uwe
Other Authors: Technische Universität Dresden, Fakultät Informatik
Format: Others
Language:English
Published: Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden 2012
Subjects:
Maschinelles Lernen
Model Kontext Optimierung
Maschine Learning
Concept Drift
Model Context Optimization
ddc:004
rvk:SS 5514
Online Access:http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83954
http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83954
http://www.qucosa.de/fileadmin/data/qucosa/documents/8395/PCD_Technical_Report.pdf

Internet

http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83954
http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83954
http://www.qucosa.de/fileadmin/data/qucosa/documents/8395/PCD_Technical_Report.pdf

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