Subset Selection as Search with Probabilistic Estimates

Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading o accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature t into this search problem as simple hill climbing approaches, and conduct a small experiment using a bestrst search technique.