Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity
Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate norma...
Saved in:
Main Author: | Autcha Araveeporn |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | International Journal of Mathematics and Mathematical Sciences |
Online Access: | http://dx.doi.org/10.1155/2022/7829795 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Linear discriminant analysis of interclass correlated spatio-temporal data
by: Jūratė Šaltytė-Benth, et al.
Published: (2003-12-01) -
Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator
by: Adewale F. Lukman, et al.
Published: (2024-12-01) -
Solution of Linear and Quadratic Equations Based on Triangular Linear Diophantine Fuzzy Numbers
by: Naveed Khan, et al.
Published: (2021-01-01) -
Generalized Quadratic Linearization of Machine Models
by: Parvathy Ayalur Krishnamoorthy, et al.
Published: (2011-01-01) -
Kibria–Lukman Hybrid Estimator for Handling Multicollinearity in Poisson Regression Model: Method and Application
by: Hleil Alrweili
Published: (2024-01-01)