A COMPARISON OF MULTIVARIATE DISCRIMINATION OF BINARY DATA (Published)
The use of classification rules for binary variables are discussed and evaluated. R-software procedures for discriminant analysis are introduced and analyzed for their use with discrete data. Methods based on the full multinomial, optimal, maximum likelihood rule and nearest neighbour procedures are treated. The results obtained ranked the procedures as follows: optimal, maximum likelihood, full multinomial and nearest neighbour rule.
Keywords: Binary Data, Classification Rules, Full Multinomial, Nearest Neighbour, Optimal, maximum likelihood