On Improving the Performance of Classification Techniques.
This study is based on the antecedent characteristics of 790 students enrolled in a professional degree program. It investigates two approaches toward improving the performance of classification techniques. The first approach focuses attention on the selection of predictors using scaled distances and intra-population correlations instead of t-ratios. The second involves the development of a Bayesian Taxonomic Procedure in addition to the traditional technique, the Linear Discriminant Function. It was found that reliance on t-ratios alone will not guarantee the selection of the best predictor set. As for the relative performance of the classification techniques, no significant difference was observed. However, certain hypotheses concerning the strength and weakness of each technique emerged from the study.