Interpreting Black-box Machine Learning Models for High Dimensional Datasets
| Document Type: | Conference Object |
|---|---|
| Language: | English |
| Author: | Md. Rezaul Karim, Md Shajalal, Alexander Graß, Till Döhmen, Sisay Adugna Chala, Alexander Boden, Christian Beecks, Stefan Decker |
| Parent Title (English): | 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 09-13 October 2023, Thessaloniki, Greece |
| Number of pages: | 10 |
| First Page: | 1 |
| Last Page: | 10 |
| ISBN: | 979-8-3503-4503-2 |
| DOI: | https://doi.org/10.1109/DSAA60987.2023.10302562 |
| Publisher: | IEEE |
| Date of first publication: | 2023/11/06 |
| Copyright: | ©2023 IEEE |
| Funding: | This paper is a collaborative effort and based on the PhD thesis [17] by the first author and the second author’s work as part of the Marie Sktodowska-Curie project funded by the Horizon Europe 2020 research and innovation program of the European Union under the grant agreement no. 955422. |
| Keywords: | Attention mechanism; Black-box models; Curse of dimensionality; Interpretability; Model surrogation |
| Departments, institutes and facilities: | Fachbereich Wirtschaftswissenschaften |
| Institut für Verbraucherinformatik (IVI) | |
| Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 005 Computerprogrammierung, Programme, Daten |
| Entry in this database: | 2023/11/13 |
