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SIGMOD
2006
ACM

An automatic construction and organization strategy for ensemble learning on data streams

9 years 3 months ago
An automatic construction and organization strategy for ensemble learning on data streams
As data streams are gaining prominence in a growing number of emerging application domains, classification on data streams is becoming an active research area. Currently, the typical approach to this problem is based on ensemble learning, which learns basic classifiers from training data stream and forms the global predictor by organizing these basic ones. While this approach seems successful to some extent, its performance usually suffers from two contradictory elements existing naturally within many application scenarios: firstly, the need for gathering sufficient training data for basic classifiers and engaging enough basic learners in voting for bias-variance reduction; and secondly, the requirement for significant sensitivity to concept-drifts, which places emphasis on using recent training data and up-to-date individual classifiers. It results in such a dilemma that some algorithms are not sensitive enough to concept-drifts while others, although sensitive enough, suffer from un...
Yi Zhang, Xiaoming Jin
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2006
Where SIGMOD
Authors Yi Zhang, Xiaoming Jin
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