Accession Number : ADP007140
Title : Comparative Study of Six Classification Methods for Mixtures of Variables,
Corporate Author : MONTREAL UNIV (QUEBEC) DEPARTEMENT D'INFORMATIQUE
Personal Author(s) : Cherkaoui, O. ; Cleroux, R.
Report Date : 1992
Pagination or Media Count : 4
Abstract : The performance of six discriminant methods is compared on simulated data consisting of mixtures of continuous, binary, ordinal and nominal variables. These methods are: Fisher's linear discrimination, logistic discrimination, quadratic discrimination, a kernel model, an independence model and the K-nearest neighbor method. In this paper, the simulation design was carefully conceived. The independence model with an association parameter performs well and is very robust.
Descriptors : *ALGEBRA, *DISCRIMINATION, *LOGISTICS, MIXTURES, MODELS, ORGANIZATIONS, PAPER, PARAMETERS, SIMULATION, VARIABLES.
Subject Categories : Statistics and Probability
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Distribution Statement : APPROVED FOR PUBLIC RELEASE