Abstract
AbstractThis paper reports the results of an experimental comparison between a linear programming (LP) approach and Fisher's linear discriminant function (FLDF) for solving discriminant analysis problems. In particular, we examine the two‐group, two‐variable problem and the associated error rates for the two groups when each of the models is applied to various sets of simulated data. In contrast to previous research, we concentrate on individual error rates for the two groups, i.e. we count the number of group 1 values classified into group 2 and the number of group 2 values classified into group 1. The results indicate that not only is FLDF better at overall classification but it also provides a much better balance between error rates for the two groups.
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