Friday 29th of March 2024
 

A Model Classification Technique for Linear Discriminant Analysis for Two Groups


Friday Zinzendoff Okwonu and Abdul Rahman Othman

Linear discriminant analysis introduced by Fisher is a known dimension reduction and classification approach that has received much attention in the statistical literature. Most researchers have focused attention on its deficiencies. As such different versions of classification procedures have been introduced for various applications. In this paper, we attempt not to robustify the Fisher linear discriminant analysis but to propose a comparable model for dimension reduction and classification. The proposed model is investigated and compared with Nearest mean classifier and Fisher classification rule using unscaled normal and scaled normal generated data. Numerical simulations reveal that the proposed model performed exactly as Fishers approach and outperformed nearest mean classifier.

Keywords: Fisher Linear Discriminant Analysis,FZOARO NMC, Classification, Hit-Ratio

Download Full-Text


ABOUT THE AUTHORS

Friday Zinzendoff Okwonu
SCHOOL OF DISTANCE EDUCATION, UNIVERSITI SAINS MALAYSIA,11800,PULAU PINANG,MALAYSIA. DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE,DELTA STATE UNIVERISTY,P.M.B.1,ABRAKA,NIGERIA

Abdul Rahman Othman
Univsersiti Sains Malaysia, 11800, Pulau Pinang, Malaysia


IJCSI Published Papers Indexed By:

 

 

 

 
+++
About IJCSI

IJCSI is a refereed open access international journal for scientific papers dealing in all areas of computer science research...

Learn more »
Join Us
FAQs

Read the most frequently asked questions about IJCSI.

Frequently Asked Questions (FAQs) »
Get in touch

Phone: +230 911 5482
Email: info@ijcsi.org

More contact details »