Accession Number:

ADP007140

Title:

Comparative Study of Six Classification Methods for Mixtures of Variables,

Descriptive Note:

Corporate Author:

MONTREAL UNIV (QUEBEC) DEPARTEMENT D'INFORMATIQUE

Personal Author(s):

• Cherkaoui, O.
• Cleroux, R.

Report Date:

1992-01-01

Pagination or Media Count:

4.0

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 Fishers 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
• Logistics, Military Facilities and Supplies

Distribution Statement:

APPROVED FOR PUBLIC RELEASE