Rich Caruana, Dayne Freitag
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.