Randomized Variable Elimination

David J. Stracuzzi, Paul E. Utgoff; 5(Oct):1331--1362, 2004.

Abstract

Variable selection, the process of identifying input variables that are relevant to a particular learning problem, has received much attention in the learning community. Methods that employ a learning algorithm as a part of the selection process (wrappers) have been shown to outperform methods that select variables independently from the learning algorithm (filters), but only at great computational expense. We present a randomized wrapper algorithm whose computational requirements are within a constant factor of simply learning in the presence of all input variables, provided that the number of relevant variables is small and known in advance. We then show how to remove the latter assumption, and demonstrate performance on several problems.

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