Title
Full interpretable machine learning in 2D with inline coordinates
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-5-2021
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.
Recommended Citation
Kovalerchuk, B., & Phan, H. (2021, July). Full interpretable machine learning in 2D with inline coordinates. 2021 25th International Conference Information Visualisation (IV). https://doi.org/10.1109/iv53921.2021.00038
Journal
2021 25th International Conference Information Visualisation (IV)
Rights
Copyright © 2021, IEEE
Comments
This article was originally published in 2021 25th International Conference Information Visualisation (IV). The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.