Full interpretable machine learning in 2D with inline coordinates

• URL: http://arxiv.org/abs/2106.07568v1
• Date: Mon, 14 Jun 2021 16:21:06 GMT
• Title: Full interpretable machine learning in 2D with inline coordinates
• Authors: Boris Kovalerchuk, Hoang Phan
• Abstract summary: It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. The classification and regression algorithms based on these inline coordinates were introduced.
• Score: 9.13755431537592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. Specifically, it can be done with the inline based coordinates in different modifications, including static and dynamic ones. The classification and regression algorithms based on these inline coordinates were introduced. A successful case study based on a benchmark data demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning as a promising ML methodology. It has advantages of abilities to involve actively the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.

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