Abstract: A two-class classification/prediction model is generated in a simple operation by performing two-class classification with a classification rate substantially close to 100%. The two-class classification/prediction model is generated by 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 sample contained in the training sample set, b) calculating a discriminant score for each training sample by using the obtained discriminant function, c) determining, based on the calculated discriminant score, whether the 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 the training sample set, e) constructing a new training sample set by extracting the training samples contained in the misclassified-sample region, and f) repeating a) to e) for the new training sample set.

Abstract: A two-class classification/prediction model is generated in a simple operation by performing two-class classification with a classification rate substantially close to 100%. The two-class classification/prediction model is generated by 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 sample contained in the training sample set, b) calculating a discriminant score for each training sample by using the obtained discriminant function, c) determining, based on the calculated discriminant score, whether the 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 the training sample set, e) constructing a new training sample set by extracting the training samples contained in the misclassified-sample region, and f) repeating a) to e) for the new training sample set.