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The aim is to address the challenges of decision-making in soft sets by developing efficient parameter reduction algorithms.

March 25, 20260 citationsOpen Access

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Unit Similarity-Based Algorithms for Efficient Parameter Reduction in Soft Sets

MIMusa Lawal Ibrahim

Key Points

The aim is to address the challenges of decision-making in soft sets by developing efficient parameter reduction algorithms.
Introduce the concept of reduct in soft sets.
Develop a new algorithm for normal parameter reduction using a unit similarity matrix.
Compare the new algorithm's effectiveness against existing parameter reduction algorithms.
The new algorithm reduces unnecessary parameters effectively.
It demonstrates lower computational complexity compared to previous methods.
Improves overall decision-making efficiency in soft set applications.

Abstract

Soft set has been introduced to deal with uncertainty involved in many real life problems. However, most of the time, these decision-making problems involve less important and redundant parameters, which make the decision making process more complex and challenging. Therefore, in this study the concept of reduct of a soft set is discussed and a new algorithm is developed for normal parameter reduction (NPR) base on the unit similarity matrix. Finally, the propose algorithm is compared with previous parameter reduction algorithms in terms of computational complexity

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Musa Lawal Ibrahim (Fri,) studied this question.

synapsesocial.com/papers/69c37b93b34aaaeb1a67e0e6 https://doi.org/https://doi.org/10.5281/zenodo.19187305

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