1. An information processing system for selecting features from a feature space, the information processing system comprising: a memory; a processor communicatively coupled to the memory; and a feature selection module communicatively coupled to the memory and the processor, wherein the feature selection module is configured to perform a method comprising: obtaining, by a processor, a set of training samples and a set of test samples, wherein the set of training samples comprises a first set of features and a class value, and wherein the set of test samples comprises a second set of features, where the second set of features is the first set of features absent the class value; determining, for each of a plurality of unselected features in a plurality of features comprising the first and second set of features, a relevancy with respect to the class value based on the set of training samples; determining, for each of the plurality of unselected features, a redundancy with respect to the plurality of features based on both the set of training samples and the set of test samples; selecting a set of features from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features, wherein the selecting is performed based on; max x j ∈ X - S m - 1 ⁢ [ I ⁡ ( x j training ; c training ) - 1 m - 1 ⁢ ∑ x i ∈ S m - 1 ⁢ I ⁡ ( x j training + test ; c training + test ) ] , where x j is a jth feature that is sample independent, x training is a jth feature based on the set of training samples, x j training+test is a jth based on the set of training samples and the set of test samples, i is an integer, X is a set of all features, S m-1 is a set of m−1 features, c is the class value, and I is mutual information; and programming a processor to perform at least one of a set of classification operations and a set of regression operations based on the set of features that have been selected.