3 citations · 5 across the 6 of their papers we have counts for
7 papers
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…
Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates
Rose McDonald, Boris Kovalerchuk
It is challenging for humans to enable visual knowledge discovery in data with more than 2-3 dimensions with a naked eye. This chapter explores the efficiency of discovering predic…
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning
Sridevi Narayana Wagle, Boris Kovalerchuk
Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. They fail to explain the model in terms of the domain they…
Discovering Interpretable Machine Learning Models in Parallel Coordinates
Boris Kovalerchuk, Dustin Hayes
This paper contributes to interpretable machine learning via visual knowledge discovery in parallel coordinates. The concepts of hypercubes and hyper-blocks are used as easily unde…