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» Learning with Drift Detection
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ASC
2008
13 years 6 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
ICDM
2008
IEEE
145views Data Mining» more  ICDM 2008»
14 years 20 days ago
Paired Learners for Concept Drift
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas a reactive learner predicts ba...
Stephen H. Bach, Marcus A. Maloof
DEXA
2008
Springer
123views Database» more  DEXA 2008»
13 years 8 months ago
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
Abstract. We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effectiv...
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
SAC
2005
ACM
13 years 11 months ago
Learning decision trees from dynamic data streams
: This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental a...
João Gama, Pedro Medas, Pedro Pereira Rodri...
MIR
2005
ACM
129views Multimedia» more  MIR 2005»
13 years 11 months ago
Tracking concept drifting with an online-optimized incremental learning framework
Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series...
Jun Wu, Dayong Ding, Xian-Sheng Hua, Bo Zhang