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