Web Reference: This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a feature subset in a greedy fashion. At each stage, this estimator chooses the best feature to add or remove based on the cross-validation score of an estimator. We propose a reverse selection design in which, instead of multiple treatment arms, we select among multiple candidate control arms. We show this design offers a reduction in required sample size compared to alternative designs and the true “best” control arm is likely to be selected. Which subset of features should be used for the best classification? How to find the best subset of size m? For d=24 and m=12, there are about 2.7 million possible feature subsets!
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