1. A sample class classification/prediction method, comprising performing operations in a programmed processor, the operations including: constructing a set of training samples, each sample including a plurality of explanatory variables and a measured value indicating whether the sample belongs to a first known class or a second known class; constructing an unknown sample set of at least one unknown sample, each sample including a plurality of explanatory variables; obtaining a discriminant function that assigns training samples to a first predicted class or a second predicted class on the basis of the plurality of explanatory variables and the measured value; calculating the ratio of the number of training samples to the number of parameters in the discriminant function, thereby calculating a reliability metric; terminating the classification/prediction if the reliability metric is less than a predetermined threshold or if the unknown sample set is empty, and otherwise calculating a discriminant score for each of the training samples and a discriminant score for an each of the unknown samples by using said discriminant function; selecting a plurality of unknown samples having a discriminant score greater than the largest discriminant score of the training sample set, assigning these selected unknown samples to the first predicted class, and removing these selected samples from the unknown sample set; selecting a plurality of unknown samples having a discriminant score less than the smallest discriminant score of the training sample set, assigning these selected unknown samples to the second predicted class, and removing these selected samples from the unknown sample set; constructing a new set of training samples by removing at least the training sample having the largest discriminant score and the training sample having the smallest discriminant score; and repeating the steps of obtaining a discriminant function through constructing a new set of training samples.