ML Anthology

Visual Analytics for Guiding Feature Attribution Method Selection

Priscylla Silva
IJCAI 2025 pp. 10977-10978
doi:10.24963/IJCAI.2025/1243 /ijcai/2025/silva2025ijcai-visual/

Abstract

Feature attribution methods explain model predictions by assigning importance to input features. Choosing the most suitable method is difficult due to inconsistencies and a lack of universal solutions. Our research focuses on proposing tools and methodologies for evaluating, comparing, and selecting these methods.

PDF IJCAI Semantic Scholar

Cite

Text

Silva. "Visual Analytics for Guiding Feature Attribution Method Selection." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1243

Markdown

[Silva. "Visual Analytics for Guiding Feature Attribution Method Selection." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/silva2025ijcai-visual/) doi:10.24963/IJCAI.2025/1243

BibTeX

@inproceedings{silva2025ijcai-visual,
  title     = {{Visual Analytics for Guiding Feature Attribution Method Selection}},
  author    = {Silva, Priscylla},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {10977-10978},
  doi       = {10.24963/IJCAI.2025/1243},
  url       = {https://mlanthology.org/ijcai/2025/silva2025ijcai-visual/}
}