Linear discriminant analysis using genetic algorithms
Aaron H. Konstam · 1993
Our goal was to develop an algorithm for classification by line= diwrimm " mt analysis using genetic slgorifhms.This algorithm W= described and tested on hth theoretid and redworld da@ as well as compared to some more classical classification algorithms.Our experiments showed that our algorithm for linear dimiminant analysis gave compamble results to other commonly used methods for solving the classification problem.It has an advantage over pamtnetic methods inthatit does notdepemd unknowing or being ableto predict the distriition parameters of the objects beiig classified.The algorithm we propose turns out to be both robust snd efficient.