3 citations · 5 across the 9 of their papers we have counts for
14 papers · 1 filter
Fully Explainable Classification Models Using Hyperblocks
Austin Snyder, Ryan Gallagher, Boris Kovalerchuk
Building on existing work with Hyperblocks, which classify data using minimum and maximum bounds for each attribute, we focus on enhancing interpretability, decreasing training tim…
Boosting of Classification Models with Human-in-the-Loop Computational Visual Knowledge Discovery
Alice Williams, Boris Kovalerchuk
High-risk artificial intelligence and machine learning classification tasks, such as healthcare diagnosis, require accurate and interpretable prediction models. However, classifier…
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
Alice Williams, Boris Kovalerchuk
Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This pap…
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…