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A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis *
Author(s) -
Nath Ravinder,
Jones Thomas W.
Publication year - 1988
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.1540-5915.1988.tb00286.x
Subject(s) - jackknife resampling , linear discriminant analysis , optimal discriminant analysis , selection (genetic algorithm) , feature selection , computer science , variable (mathematics) , discriminant , variables , artificial intelligence , machine learning , mathematics , statistics , data mining , mathematical analysis , estimator
There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.