Abstract
This paper presents a new framework for dealing with two main types of concept drift: sudden and gradual drift in labelled data with decision attribute. The learning examples are processed in batches of the same size. This new framework, called Batch Weighted Ensemble, is based on incorporating drift detector into the evolving ensemble. Its performance was evaluated experimentaly on data sets with different types of concept drift and compared with the performance of a standard Accuracy Weighted Ensemble classifier. The results show that BWE improves evaluation measures like processing time, memory used and obtain competitive total accuracy.
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Deckert, M. (2011). Batch Weighted Ensemble for Mining Data Streams with Concept Drift. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_32
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DOI: https://doi.org/10.1007/978-3-642-21916-0_32
Publisher Name: Springer, Berlin, Heidelberg
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