5 citations · 14 across the 19 of their papers we have counts for
4 papers · 1 filter
Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery
Boris Kovalerchuk, Elijah McCoy
Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a longstanding problem. Many ML algorithms are not applicable to mixed data, which include numeric and n…
Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models
Alex Worland, Sridevi Wagle, Boris Kovalerchuk
Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a…
Interpretable Machine Learning for Self-Service High-Risk Decision-Making
Charles Recaido, Boris Kovalerchuk
This paper contributes to interpretable machine learning via visual knowledge discovery in general line coordinates (GLC). The concepts of hyperblocks as interpretable dataset unit…
Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
Boris Kovalerchuk, Răzvan Andonie, Nuno Datia +2
This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/V…