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Peer reviewedMeshbane, Alice; Morris, John D. – Journal of Experimental Education, 1995
A method for comparing the cross-validated classification accuracies of linear and quadratic classification rules is presented under varying data conditions for the "k"-group classification problem. Separate-group and total-group proportions of correct classifications can be compared for the two rules, as is illustrated. (Author/SLD)
Descriptors: Classification, Comparative Analysis, Discriminant Analysis, Equations (Mathematics)
Download full textMeshbane, Alice; Morris, John D. – 1997
A method for comparing the cross-validated classification accuracy of Fisher's linear classification functions (FLCFs) and the least absolute deviation is presented under varying data conditions for the two-group classification problem. With this method, separate-group as well as total-sample proportions of current classifications can be compared…
Descriptors: Classification, Comparative Analysis, Computer Software, Correlation
Meshbane, Alice; Morris, John D. – 1995
Cross-validated classification accuracies were compared under assumptions of equal and varying degrees of unequal prior probabilities of group membership for 24 bootstrap and 48 simulated data sets. The data sets varied in sample size, number of predictors, relative group size, and degree of group separation. Total-group hit rates were used to…
Descriptors: Classification, Comparative Analysis, Discriminant Analysis, Group Membership
Meshbane, Alice; Morris, John D. – 1994
A method for comparing the cross validated classification accuracies of linear and quadratic classification rules is presented under varying data conditions for the k-group classification problem. With this method, separate-group as well as total-group proportions of correct classifications can be compared for the two rules. McNemar's test for…
Descriptors: Classification, Comparative Analysis, Correlation, Discriminant Analysis
