This article is about wrappers in data mining. For other meanings see wrapper.

In data mining and treatment learning, wrappers are trainable modules that parse text looking for structure. They are often created by a programmer for specific tasks, or, in wrapper induction, can be programmed to train themselves. The idea is to wrap their treatments learners in a preprocessor that would search to make subsets from the current set of attributes. Using wrappers, the attribute subset would continue to grow until the accuracy of the model was no longer more accurate.[1]

The TAR2 treatment learner used by Menzies and Hu did not use wrappers as it claimed their use made the treatment learning process too slow.[2]


  1. Kohavi R, John GH (1997). "Wrappers for feature subset selection". Artificial Intelligence 97 (1-2): pp. 273–324. 
  2. Menzies T, Hu Y (October 2003). "Data mining for busy people". IEEE Computer: pp. 18–25. 

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