A Quality Metric to Improve Scatterplots for Explainable AI
| dc.contributor.author | Liu, Liqun | en_US |
| dc.contributor.author | Ruddle, Roy A. | en_US |
| dc.contributor.author | Bogachev, Leonid V. | en_US |
| dc.contributor.author | Rezaei, Mahdi | en_US |
| dc.contributor.author | Khara, Arjun | en_US |
| dc.contributor.editor | Kucher, Kostiantyn | en_US |
| dc.contributor.editor | Diehl, Alexandra | en_US |
| dc.contributor.editor | Gillmann, Christina | en_US |
| dc.date.accessioned | 2024-05-21T08:44:55Z | |
| dc.date.available | 2024-05-21T08:44:55Z | |
| dc.date.issued | 2024 | |
| dc.description.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. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | EuroVis 2024 - Posters | |
| dc.identifier.doi | 10.2312/evp.20241077 | |
| dc.identifier.isbn | 978-3-03868-258-5 | |
| dc.identifier.pages | 3 pages | |
| dc.identifier.uri | https://doi.org/10.2312/evp.20241077 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/evp20241077 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Human-centered computing → Visualisation design and evaluation methods; Computing methodologies → Machine learning | |
| dc.subject | Human centered computing → Visualisation design and evaluation methods | |
| dc.subject | Computing methodologies → Machine learning | |
| dc.title | A Quality Metric to Improve Scatterplots for Explainable AI | en_US |