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Refining Two‐Group Multivariable Classification Models Using Univariate Optimal Discriminant Analysis *
Author(s) -
Yarnold Paul R.,
Soltysik Robert C.
Publication year - 1991
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1991.tb01912.x
Subject(s) - linear discriminant analysis , univariate , optimal discriminant analysis , discriminant , mathematics , multivariable calculus , statistics , group (periodic table) , multiple discriminant analysis , artificial intelligence , multivariate statistics , pattern recognition (psychology) , computer science , engineering , chemistry , organic chemistry , control engineering
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.