Feature Subset Selection as Search with Probabilistic Estimates
Ron Kohavi · 1994
Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We for-search problem with probabilistic estimates. Search-ing a space using an evaluation function that is a random variable requires trading o accuracy of es-timates for increased state exploration. We show how recent feature subset selection algorithms in the ma-chine learning literature t into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-rst search technique. 1