  Feature selection with high-dimensional data and a very small proportion of
relevant features poses a severe challenge to standard statistical methods. We
have developed a new approach (HARVEST) that is straightforward to apply,
albeit somewhat computer-intensive. This algorithm can be used to pre-screen a
large number of features to identify those that are potentially useful. The
basic idea is to evaluate each feature in the context of many random subsets of
other features. HARVEST is predicated on the assumption that an irrelevant
feature can add no real predictive value, regardless of which other features
are included in the subset. Motivated by this idea, we have derived a simple
statistical test for feature relevance. Empirical analyses and simulations
produced so far indicate that the HARVEST algorithm is highly effective in
predictive analytics, both in science and business.
