This paper is a study of two-group classification models for binary variables. Eight classification procedures for binary variables are discussed and evaluated at each of 118 configurations of the sampling experiments. The results obtained ranked the procedures as follows: Optimal, Linear discriminant, Maximum likelihood, Predictive, Dillon Goldstein, Full multinomial, Likelihood and Nearest neighbour. Also the result of the study show that increase in the number of variables improve the accuracy of the models.
Comparison of Jackknife and Resubstitution Methods in the Estimation of Error Rates in Discriminant Analysis (Published)
Most often, in classification procedures, error rates or probability of misclassification are assessed. Because in real life application of classification rules or methods, some errors of misclassification can be more costly than the others. In this work, two methods of estimating error rates, namely; the Jackknife and resubstitution methods are examined using ten samples of size from the population pair From the results obtained from the experiments, we observed that the resubsitution method performed better than the Jackknife method in estimating the exact probabilities of misclassification