1. An information processing apparatus comprising: a processor configured to execute: a relevance evaluator that evaluates relevance E 1 by calculating relevance of each feature S i included in a set of features by E 1 (s i , X (i) , y), where S i indicates selection or non-selection of the feature, X (i) is the sample group including only the i th feature, and y is class level; a redundancy evaluator that evaluates redundancy E 2 by calculating redundancy between the features included in the set of features by E 2 (s i , s j , X (i) , X (j) , y), wherein S j indicates selection or non-selection of the feature, X (j) is the sample group including only the j th feature and the redundancy E2 satisfies a submodularity represented by 
 E 2 (0,0, X (i) ,X (j) ,y )+ E 2 (1,1, X (i) ,X (j) ,y )≦ E 2 (0,1, X (i) ,X (j) ,y )+ E 2 (1,0, X (i) ,X (j) ,y ); and a selected feature determiner that determines selected features that minimize a submodular objective function E defined as a sum of the relevance E 1 calculated by said relevance evaluator and the redundancy E 2 calculated by said redundancy evaluator wherein the submodular objective function E defined by E ⁡ ( s ) = - ∑ i ⁢ E 1 ⁡ ( s i , X ( i ) , y ) + λ ⁢ ∑ i , j ⁢ E 2 ⁡ ( s i , s j , X ( i ) , X ( j ) , y ) where λ is a positive constant that determines a relative weight of two terms corresponding to the i th feature and the j th feature.
3. The information processing apparatus according to claim 1 , wherein said selected feature determiner optimizes the submodular objective function formed from a term using two features at maximum as arguments to determine the selected features.