1. A method for generating a two-class classification/prediction model, comprising: a) obtaining a discriminant function for classifying a training sample set into two predetermined classes on the basis of an explanatory variable generated for each individual training sample contained in said training sample set; b) calculating a discriminant score for each training sample by using said obtained discriminant function; c) based on said calculated discriminant score, determining whether said training sample is correctly classified or not; d) determining a misclassified-sample region based on maximum and minimum discriminant scores taken from among misclassified samples in said training sample set; e) constructing a new training sample set by extracting said training samples contained in said misclassified-sample region; f) repeating said a) to e) for said new training sample set; and g) storing, as a two-class classification/prediction model for samples of unknown classes, a plurality of discriminant functions obtained as a result of said repetition and misclassified-sample region information associated with each of said discriminant functions.