  Existing feature selection methods fail to properly account for interactions
between features when evaluating feature subsets. In this paper, we attempt to
remedy this issue by using orthogonal variance decomposition to evaluate
features. The orthogonality of the decomposition allows us to directly
calculate the total contribution of a feature to the output variance. Thus we
obtain an efficient algorithm for feature evaluation which takes into account
interactions among features. Numerical experiments demonstrate that our method
accurately identifies relevant features and improves the accuracy of numerical
models.
