5 citations · 14 across the 19 of their papers we have counts for
20 papers
LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
Boris Kovalerchuk, Brent D. Fegley
When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model absence. RAG and knowledge-graph methods…
High-Dimensional Data Classification in Concentric Coordinates
Alice Williams, Boris Kovalerchuk
The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occ…
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…
General Line Coordinates in 3D
Joshua Martinez, Boris Kovalerchuk
Interpretable interactive visual pattern discovery in lossless 3D visualization is a promising way to advance machine learning. It enables end users who are not data scientists to…
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…
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…
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…
Full interpretable machine learning in 2D with inline coordinates
Boris Kovalerchuk, Hoang Phan
This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to de…
Deep Learning Image Recognition for Non-images
Boris Kovalerchuk, Divya Chandrika Kalla, Bedant Agarwal
Powerful deep learning algorithms open an opportunity for solving non-image Machine Learning (ML) problems by transforming these problems to into the image recognition problems. Th…