How Useful is Relevance (1994) [4 citations — 0 self]
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by Rich Caruana, Dayne Freitag
In: Relevance, Papers from the 1994 AAAI Fall Symposium
http://www.fac.cs.cmu.edu/afs/cs.cmu.edu/user/dayne/www/ps/rel94.ps.Z
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Abstract:
Eliminating irrelevant attributes prior to induction boosts the performance of many learning algorithms. Relevance, however, is no guarantee of usefulness to a particular learner. We test two methods of finding relevant attributes, FOCUS and RELIEF, to see how the attributes they select perform with ID3/C4.5 on two learning problems from a calendar scheduling domain. A more direct attribute selection procedure, hillclimbing in attribute space, finds superior attribute sets. 1
