Ideas from Wrappers for Feature Subset Selection

  • Paper is as survey of feature selection algorithms, was written in ’96 so its probably dated by now.
  • Discussion of various definitions of relevance/irrelevance.
  • Demonstration that in imperfect learners (as opposed to bayes optimal) relevance of a feature doesn’t necessitate that it is in the optimal feature subset, conversely that irrelevance doesn’t imply it shouldn’t be in the optimal feature subset.  These are basically due to restrictions in the hypothesis space real algorithms have.
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