A Quality Metric to Improve Scatterplots for Explainable AI
Liu, L., Ruddle, R.A., Bogachev, L.V. et al. (2 more authors)
(2024)
A Quality Metric to Improve Scatterplots for Explainable AI.
In:
EuroVisPosters2024.
EuroVis 2024 - 26th EG Conference on Visualization, 27-31 May 2024, Odense, Denmark.
The Eurographics Association
Abstract
Scatterplots are widely utilised in Explainable Artificial Intelligence (XAI) to investigate misclassifications and patterns among instances. However, when datasets are large, overplotting diminishes the effectiveness of scatterplots. This poster introduces a new quality metric to measure the overplotting of scatterplots in the context of XAI. Initially, we assess the significance of each data point within a scatterplot by continuous density transformation, Mahalanobis Distance and a mapping function. Building on this foundation, we develop a quality metric for scatterplots. Our metric performs well accounting for rendering orders and marker sizes in scatterplots, showcasing the metric's potential to improve the effectiveness of XAI scatterplots.
Metadata
Item Type:
Proceedings Paper
Authors/Creators:
Liu, L.
Ruddle, R.A.
Bogachev, L.V.
Rezaei, M.
Khara, A.
Copyright, Publisher and Additional Information:
This is an open access conference paper under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)