- AutorIn
- Nico Hoffmann
- Dipl. Inf. Matthias Kirmse
- Dr.-Ing. habil. Uwe Petersohn
- Titel
- Approaching Concept Drift by Context Feature Partitioning
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa-83954
- Schriftenreihe
- Technische Berichte
- Bandnummer
- 2012,03 (TUD-FI12-03-Februar 2012)
- Erstveröffentlichung
- 2012
- ISSN
- 1430-211X
- Abstract (EN)
- 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 classify new instances accordingly. To determine the optimal partitioning for a domain we apply a search algorithm aiming to maximize the resulting accuracy. In practical domains like fault detection concept drift often occurs in combination with imbalances data. As this issue gets more important learning models on smaller subdomains we additionally use sampling methods to handle it. Comparative experiments with artificial data sets showed that our approach outperforms a plain SVM regarding different performance measures. Summarized, the partitioning concept drift approach (PCD) is a possible way to handle concept drift in domains where the causing context features are at least partly known.
- Freie Schlagwörter (DE)
- Maschinelles Lernen, Model Kontext Optimierung
- Freie Schlagwörter (EN)
- Maschine Learning, Concept Drift, Model Context Optimization
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- SS 5514
- Publizierende Institution
- Technische Universität Dresden, Dresden
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa-83954
- Veröffentlichungsdatum Qucosa
- 20.02.2012
- Dokumenttyp
- Forschungsbericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis