The Wrapper Approach

        Ron Kohavi                        George H. John
        Data Mining and Visualization     Data Mining
        Silicon Graphics, Inc             Epiphany, Inc
        2011 N. Shoreline Blvd.           2300 Geng Road, Suite 200
        Mountain View, CA 94043           Palo Alto, CA 94303
        ronnyk@sgi.com                    gjohn@cs.stanford.edu
        robotics.stanford.edu/~ronnyk     robotics.stanford.edu/~gjohn 


  In the feature subset selection problem, a learning algorithm is
  faced with the problem of selecting a relevant subset of features
  upon which to focus its attention, while ignoring the rest.  To
  achieve the best possible performance with a particular learning
  algorithm on a particular training set, a feature subset selection
  method should consider how the algorithm and the training set
  interact.  We explore the relation between optimal feature subset
  selection and relevance.  The wrapper method searches for an optimal
  feature subset tailored to a particular algorithm and a domain.  We
  compare the wrapper approach to induction without feature subset
  selection and to Relief, a filter approach to feature subset
  selection.  Improvement in accuracy is achieved for some datasets
  for the two families of induction algorithms used: decision trees
  and Naive-Bayes.  In addition, the feature subsets selected by the
  wrapper are significantly smaller than the original subsets used by
  the learning algorithms, thus producing more comprehensible models.

  
Citation: 
  
Kohavi, Ron and John, George H. (1998) The Wrapper Approach.
In H. Liu and H. Motoda (Eds.), Feature Selection for Knowledge
Discovery in Databases.  Springer-Verlag.

Significant parts of this chapter are reprinted from Artificial
Intelligence Journal, Vol. 97, Nos. 1-2, pp. 273--324, 1997 with kind
permission from Elsevier Science -- NL, Sara Burgerhartstraat 25, 1055
KV Amsterdam, The Netherlands.