5 citations · 14 across the 19 of their papers we have counts for
5 papers · 1 filter
Full High-Dimensional Intelligible Learning In 2-D Lossless Visualization Space
Boris Kovalerchuk, Hoang Phan
This study explores a new methodology for machine learning classification tasks in 2-dimensional visualization space (2-D ML) using Visual knowledge Discovery in lossless General L…
Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling
Boris Kovalerchuk Andrew Dunn, Alex Worland, Sridevi Wagle
To increase the interpretability and prediction accuracy of the Machine Learning (ML) models, visualization of ML models is a key part of the ML process. Decision Trees (DTs) are e…
Visual Knowledge Discovery with General Line Coordinates
Lincoln Huber, Boris Kovalerchuk, Charles Recaido
Understanding black-box Machine Learning methods on multidimensional data is a key challenge in Machine Learning. While many powerful Machine Learning methods already exist, these…
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization
Boris Kovalerchuk, Elijah McCoy
Building accurate and interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focus…
Parallel Coordinates for Discovery of Interpretable Machine Learning Models
Dustin Hayes, Boris Kovalerchuk
This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made…