Document Type


Date of Degree Completion

Fall 2021

Degree Name

Master of Science (MS)


Computational Science

Committee Chair

Boris Kovalerchuk

Second Committee Member

Razvan Andonie

Third Committee Member

Szilard Vajda


This thesis explores a new approach for machine learning classification task in 2-dimensional space (2-D ML) with In-line Coordinates. This is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. In-line coordinates method allows discovering n-D patterns in 2-D space without loss of n-D information using graph representation of n-D data in 2-D. Specifically, this thesis shows that it can be done with In-line Based Coordinates in different modifications, which are defined, including static and dynamic ones. Some classification and regression algorithms based on these In-line Coordinates were explored. Two successful cases studies based on benchmark datasets (Wisconsin Breast Cancer dataset and Page Block Classification dataset) demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning with a respective methodology. In-line coordinates method has advantages to actively include the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.

Keywords— interpretable machine learning, classification, regression, visual knowledge discovery.