K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results - Data Mining: Theory, Methodology, Techniques, and Applications - page 62

is equivalent to double the weight of misclassified instances) could be subject of
exhaustive research.
Further work could focus on other classification trees construction methods,
as C4.5 [24] or Oc1 [21].
An extension of the presented approach is to select among the feature subset
that better performance presents by the classification point of view. A Feature
Subset Selection [12, 13, 26] technique can be applied in order to select which of
the predictor variables should be used. This could take advantage in the hybrid
classifier construction, as well as in the accuracy.

Acknowledgments

This work has been supported by the University of the Basque Country under
grant 1/UPV00140.226-E-15412/2003 and by the Gipuzkoako Foru Aldundia
OF-761/2003.

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