A computer-implemented feature amount selection method of selecting a feature amount group to be used for determining which of N (two or more) classes a sample belongs to, the method comprising: an input step of inputting a learning data set including a known sample group belonging to a given class, which is a target, and a feature amount group of the known sample group; and a selection step of selecting a feature amount group needed for class determination for an unknown sample of which a belonging class is unknown, from the feature amount group based on the learning data set, wherein the selection step includes a quantification step of quantifying, by a pairwise coupling that combines two classes among the N classes, a discrimination possibility between the two classes in accordance with each feature amount of the selected feature amount group by using the learning data set, and an optimization step of totalizing the quantified discrimination possibilities for all the pairwise couplings and selecting a combination of the feature amount groups for which a result of the totalization is optimized, characterized by a first marking step of marking a part of the given classes as first discrimination unneeded class groups that do not need to be discriminated from each other, and a first exclusion step of excluding the pairwise coupling in the marked first discrimination unneeded class groups from pairwise couplings to be expanded. an input step of inputting a learning data set including a known sample group belonging to a given class, which is a target, and a feature amount group of the known sample group; and a selection step of selecting a feature amount group needed for class determination for an unknown sample of which a belonging class is unknown, from the feature amount group based on the learning data set, wherein the selection step includes a quantification step of quantifying, by a pairwise coupling that combines two classes among the N classes, a discrimination possibility between the two classes in accordance with each feature amount of the selected feature amount group by using the learning data set, and an optimization step of totalizing the quantified discrimination possibilities for all the pairwise couplings and selecting a combination of the feature amount groups for which a result of the totalization is optimized, characterized by a first marking step of marking a part of the given classes as first discrimination unneeded class groups that do not need to be discriminated from each other, and a first exclusion step of excluding the pairwise coupling in the marked first discrimination unneeded class groups from pairwise couplings to be expanded. a quantification step of quantifying, by a pairwise coupling that combines two classes among the N classes, a discrimination possibility between the two classes in accordance with each feature amount of the selected feature amount group by using the learning data set, and an optimization step of totalizing the quantified discrimination possibilities for all the pairwise couplings and selecting a combination of the feature amount groups for which a result of the totalization is optimized, characterized by a first marking step of marking a part of the given classes as first discrimination unneeded class groups that do not need to be discriminated from each other, and a first exclusion step of excluding the pairwise coupling in the marked first discrimination unneeded class groups from pairwise couplings to be expanded.