A Thorough Survey on the Fusion of Machine Learning Algorithms and Neutrosophic Theory

Abstract

Massive, complex, and varied collections of data that are difficult to store, process, and visualize to use in later processes or results are referred to as "big data". Data mining is the process of examining and evaluating enormous volumes of data to look for important patterns and principles. Because data mining can reveal useful patterns that were previously unknown, it is essential to many human efforts. There are a lot of machine learning algorithms that are widely used in this regard. Currently, one of the most effective approaches for addressing the aforementioned problems when combined with machine learning tools is recognized as being neutrosophic set theory (NS). Generalizations of interval fuzzy sets, or neutrosophic set theory (NS), have gained significant attention in the data mining and machine learning sectors during the past ten years due to their multitude of applications. Since natural ambiguity is dealt with by neutrophilic group theory (NS), academics have been motivated to incorporate NS into machine learning algorithms in order to eliminate ambiguity from data, add more precise data values, and improve the accuracy and efficiency of mining methods. A large number of research papers have been published on the hybridization of neutrosophic set theory (NS) and machine learning techniques. This has motivated us to present a review of the literature on the application of NS with machine learning approaches to address data mining challenges in the years 2020–2024.