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Knowledge Discovery from Data Streams
book

Knowledge Discovery from Data Streams

by Joao Gama
May 2010
Intermediate to advanced content levelIntermediate to advanced
255 pages
8h 11m
English
Chapman and Hall/CRC
Content preview from Knowledge Discovery from Data Streams
Ensembles of Classifiers 163
data streams. The method builds separate classifiers from sequential batches
of training examples. These classifiers are combined into a fixed-size ensemble
using a heuristic replacement strategy.
In a similar way, Wang, Fan, Yu, and Han (2003) propose a general frame-
work for mining concept-drifting data streams using weighted ensemble clas-
sifiers. They train an ensemble of classification models, such as C4.5, RIPPER,
naive Bayes, etc., from sequential batches of the data stream. The classifiers
in the ensemble are judiciously weighted based on their expected classification
accuracy on the test data under the time-evolving environmen ...
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Publisher Resources

ISBN: 9781439826126