Techniques are described herein to efficiently detect redundant features in a machine learning process. The techniques are able to compute feature redundancy not only for a single feature at a time, but for any subset of features without the need to naively train and evaluate a classifier for each combination of features.
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Komarek, Tomas; Brabec, Jan; and Machlica, Lukas, "MACHINE LEARNING FOR THING DETECTION, BEHAVIOR ANALYTICS, AND THREAT DETECTION: AN EFFICIENT ELIMINATION METHOD OF REDUNDANT FEATURE-SUBSETS", Technical Disclosure Commons, (October 01, 2018)