The application of bias to discriminant analysis

Abstract When classification rules are constructed using sample estimatest it is known that the probability of misclassification is not minimized. This article introduces a biased minimum X2 rule to classify items from a multivariate normal population. Using the principle of variance reduction, the probability of misclassification is reduced when the biased procedure is employed. Results of sampling experiments over a broad range of conditions are provided to demonstrate this improvement. Keywords: classification proceduresridge applicationsbiased classification proceduresbiased discriminant analysisvariance reductiondiscriminant analysis