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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 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.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:25916
Date20 February 2012
CreatorsHoffmann, Nico, Kirmse, Matthias, Petersohn, Uwe
PublisherTechnische Universität Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typedoc-type:workingPaper, info:eu-repo/semantics/workingPaper, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relationurn:nbn:de:bsz:14-qucosa-79344, qucosa:24841

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