Refining Two‐Group Multivariable Classification Models Using Univariate Optimal Discriminant Analysis*
提出用单变量最优判别分析优化Fisher判别分析得到的预测值，以最大化训练分类准确率，并通过三个例子展示该方法。
ABSTRACT Fisher's discriminant analysis (FDA) is often used to obtain a prediction model for dichotomous classifications on the basis of two or more independent variables. FDA provides an equation whereby values on independent variables are combined into a single predicted value ( Y* ) that is compared against a cutpoint and direction in order to make classifications. Theoretically, univariate optimal discriminant analysis employed on these Y* will maximize training classification accuracy. This methodology is illustrated using three examples.