Full interpretable machine learning in 2D with inline coordinates

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Department or Administrative Unit

Computer Science

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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.


This article was originally published in 2021 25th International Conference Information Visualisation (IV). The full-text article from the publisher can be found here.

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2021 25th International Conference Information Visualisation (IV)


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