Improving Partial Least Squares regression based on meta-heuristic optimization algorithms

Authors

  • Didar Abdal Wafaa Rashid Aziz Statistics and Informatics Department, College of Administration and Economics University of Sulaimani, Sulaimani, Iraq
  • Zakariya Yahya Algamal Statistics and Informatics Department, College of Administration and Economics University of Sulaimani, Sulaimani, Iraq

DOI:

https://doi.org/10.19139/soic-2310-5070-2765

Keywords:

Meta-heuristic algorithm, Partial least squares regression

Abstract

A popular method that combines and generalizes features from multiple regression and principal component analysis is called partial least squares regression (PLS). When we need to predict a collection of dependent variables from a very big number of independent factors, PLS is especially helpful. However, the performance of PLS depends on the number of components. Meta-heuristic algorithm implementation has grown in popularity among researchers. In this study, a Meta-heuristic algorithm is proposed to determine the number of components. The suggested approach will effectively assist in identifying the appropriate number of components with a strong forecast. The proposed approach is contrasted with two well-known approaches. The experimental results provide a thorough demonstration of the suggested method's superiority in prediction ability.

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Published

2026-09-08

How to Cite

Aziz , D. A. W. R., & Algamal, Z. Y. (2026). Improving Partial Least Squares regression based on meta-heuristic optimization algorithms. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2765

Issue

Online First

Section

Research Articles

License

Copyright (c) 2026 Didar Abdal Wafaa Rashid Aziz , Zakariya Yahya Algamal

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