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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

A forecasting method with efficient selection of variables in multivariate data sets.

Pinki Sagar¹, Prinima Gupta¹, Indu Kashyap¹

¹Manav Rachna International Institute of Research and Studies, Faridabad, Haryana India.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management

|March 8, 2021

Summary

This summary is machine-generated.

This study enhances polynomial regression by using the coefficient of determination (COD) for efficient variable selection. This method reduces data maintenance costs, execution time, and improves prediction accuracy in multivariate datasets.

Keywords:

And multivariate data sets Coefficient of determination (COD)Independent variable Polynomial regression

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Area of Science:

Data Science
Statistical Modeling
Machine Learning

Background:

Regression analysis models relationships between independent (x) and dependent (y) variables.
Polynomial regression extends this to nth-degree polynomials, modeling nonlinear relationships.
Multivariate datasets often contain irrelevant attributes, complicating analysis.

Purpose of the Study:

To improve polynomial regression analysis through efficient variable selection.
To reduce data maintenance costs and execution time.
To enhance prediction accuracy rates in multivariate data analysis.

Main Methods:

Utilizing the coefficient of determination (COD) for variable selection.
COD quantifies the proportion of variance in the dependent variable predictable from independent variables.
Eliminating irrelevant attributes from multivariate datasets using COD.

Main Results:

The coefficient of determination effectively identifies and removes irrelevant variables.
Improved variable selection leads to reduced dataset size and maintenance costs.
Enhanced prediction accuracy and reduced execution time were observed.

Conclusions:

Coefficient of determination is a valuable tool for optimizing polynomial regression models.
Efficient variable selection using COD enhances model performance and efficiency.
This approach is beneficial for managing and analyzing large, complex datasets.