Machine learning in Neutrosophic Environment: A Survey

Veracity in big data analytics is recognized as a complex issue in data preparation process,<br> involving imperfection, imprecision and inconsistency. Single-valued Neutrosophic numbers<br> (SVNs), have prodded a strong capacity to model such complex information. Many Data mining<br> and big data techniques have been proposed to deal with these kind of dirty data in preprocessing<br> stage. However, only few studies treat the imprecise and inconsistent information inherent in the<br> modeling stage. However, this paper summarizes all works done about mapping machine learning<br> algorithms from crisp number space to Neutrosophic environment. We discuss also contributions<br> and hybridization of machine learning algorithms with Single-valued Neutrosophic numbers<br> (SVNs) in modeling imperfect information, and then their impacts on resolving reel world problems.<br> In addition, we identify new trends for future research, then we introduce, for the first time,<br> a taxonomy of Neutrosophic learning algorithms, clarifying what algorithms are already processed<br> or not, which makes it easier for domain researchers.