Machine learning in Neutrosophic Environment: A Survey

• Main Authors: Azeddine Elhassouny, Soufiane Idbrahim, F. Smarandache
• Format: Article
• Language: English
• Published: University of New Mexico 2019-11-01
• Series: Neutrosophic Sets and Systems
• Online Access: http://fs.unm.edu/NSS/Machinelearning.pdf

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