1. A method for generating a two-class classification/prediction model, comprising: obtaining a discriminant function to classify a training sample set into two predetermined classes on a basis of an explanatory variable generated for each individual training sample contained in said training sample set; calculating a discriminant score for each training sample by using said obtained discriminant function; determining, based on said calculated discriminant score, whether said training sample is correctly classified; determining a misclassified-sample region based on maximum and minimum discriminant scores taken from among misclassified samples in said training sample set; constructing a new training sample set by extracting said training samples contained in said misclassified-sample region; repeating said obtaining, said calculating, said determining of said training sample, and said determining of said misclassified-sample region, for said new training sample set; and storing, as a two-class classification/prediction model for samples of unknown classes, a plurality of discriminant functions obtained as a result of said repeating and misclassified-sample region information associated with each of said discriminant functions.