Optimality Criteria
There are a variety of optimality criteria that can be used for controlling feature selection. The oldest are Mallows' Cp statistic and Akaike information criterion (AIC). These add variables if the t-statistic is bigger than .
Other criteria are Bayesian information criterion (BIC) which uses, minimum description length (MDL) which asymptotically uses, Bonnferroni / RIC which use, maximum dependency feature selection, and a variety of new criteria that are motivated by false discovery rate (FDR) which use something close to .
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“The Hacker Ethic: Access to computers—and anything which might teach you something about the way the world works—should be unlimited and total.
Always yield to the Hands-On Imperative!
All information should be free.
Mistrust authority—promote decentralization.
Hackers should be judged by their hacking, not bogus criteria such as degrees, age, race, or position.
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—Steven Levy, U.S. writer. Hackers, ch. 2, “The Hacker Ethic,” pp. 27-33, Anchor Press, Doubleday (1984)