The application of bias to discriminant analysis

Abstract

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