Machine learning in Neutrosophic Environment: A Survey
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2019-11-01
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| Series: | Neutrosophic Sets and Systems |
| Online Access: | http://fs.unm.edu/NSS/Machinelearning.pdf |
| Summary: | 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. |
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| ISSN: | 2331-6055 2331-608X |