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Found 15 papers, 0 papers with code
General Line Coordinates in 3D
It enables end users who are not data scientists to take control of the model development process as a self-service.
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization
This work focuses on developing numeric coding schemes for non-numeric attributes for ML algorithms to support accurate and explainable ML models, methods for lossless visualization of n-D non-numeric categorical data with visual rule discovery in these visualizations, and accurate and explainable ML models for categorical data.
Full High-Dimensional Intelligible Learning In 2-D Lossless Visualization Space
It is shown that this is a full machine learning approach that does not require processing n-dimensional data in an abstract n-dimensional space.
Parallel Coordinates for Discovery of Interpretable Machine Learning Models
It is suggested to use mixed and pure hyperblocks in the proposed data classifier algorithm Hyper.
Visual Knowledge Discovery with General Line Coordinates
These expansions are General Line Coordinates non-linear, interactive rules linear, hyperblock rules linear, and worst-case linear.
Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery
Another longstanding problem is developing algorithms for lossless visualization of multidimensional mixed data.
Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models
In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph.
Interpretable Machine Learning for Self-Service High-Risk Decision-Making
Additionally, areas of hyperblock impurity were discovered and used to establish dataset splits that highlight the upper estimate of worst-case model accuracy to guide model selection for high-risk decision-making.
Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
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/Visual Analytics.
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning
The interactive approach provides flexibility to the end user to perform data classification as self-service without having to rely on a machine learning expert.
Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates
An interactive software system EllipseVis, which is developed in this work, processes high-dimensional datasets, creates EPC visualizations, and produces predictive classification models by discovering dominance rules in EPC.
Deep Learning Image Recognition for Non-images
The computational experiments with CPC-R are conducted for different CNN architectures, and methods to optimize the CPC-R images showing that the combined CPC-R and deep learning CNN algorithms are able to solve non-image ML problems reaching high accuracy on the benchmark datasets.
Full interpretable machine learning in 2D with inline coordinates
It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space.
Discovering Interpretable Machine Learning Models in Parallel Coordinates
Another advantage of sets of HBs relative to the decision trees is the ability to avoid both data overgeneralization and overfitting.
Survey of explainable machine learning with visual and granular methods beyond quasi-explanations
Next, we present methods of visual discovery of ML models, with the focus on interpretable models, based on the recently introduced concept of General Line Coordinates (GLC).
