document: Streaming feature selection using alpha-investing In Streaming Feature Selection ( SFS ) , new features are sequentially considered for addition to a predictive model . When the space of potential features is large , SFS offers many advantages over traditional feature selection methods , which assume that all features are known in advance . Features can be generated dynamically , focusing the search for new features on promising subspaces , and overfitting can be controlled by dynamically adjusting the threshold for adding features to the model . We describe α-investing , an adaptive complexity penalty method for SFS which dynamically adjusts the threshold on the error reduction required for adding a new feature . α-investing gives false discovery rate-style guarantees against overfitting . It differs from standard penalty methods such as AIC , BIC or RIC , which always drastically over - or under-fit in the limit of infinite numbers of non-predictive features . Empirical results show that SFS is competitive with much more compute-intensive feature selection methods such as stepwise regression , and allows feature selection on problems with over a million potential features .
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