Selecting a Two-Group Classification Weighting Algorithm.

Abstract

The cross-validated classification accuracies of three predictor weighting strategies (least squares, ridge regression, and reduced rank) were compared under varying simulated data conditions for the two-group classification problem. Results were somewhat similar to previous findings with multiple regression when absolute rather than relative cross-validation accuracy was the criterion. The alternate weighting strategies performed better than the usual least squares algorithm in many of the data conditions, but the margin by which they surpassed least squares was in many cases quite small. Also, the alternate algorithms were poorer than least squares in some data configurations, and in some cases, by an appreciable margin. Verification of these simulation findings was obtained by documenting agreement with theory, and with an analytic sample-specific procedure that was used with some "real" data sets. The results of the analyses with real data sets yielded results that were similar to those from the simulation. The routine and uncritical use of biased weighting algorithms in classification as advanced by P. J. DiPillo was not supported.