ORCID: https://orcid.org/0000-0002-1231-4985; Kornowicz, Jaroslaw; Heid, Stefan; Thommes, Kirsten
ORCID: https://orcid.org/0000-0002-8057-7162 und Hüllermeier, Eyke
ORCID: https://orcid.org/0000-0002-9944-4108
(October 2023):
Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain.
Lernen, Wissen, Daten, Analysen (LWDA), Marburg, Germany, 9-11 October 2023.
Leyer, Michael und Wichmann, Johannes (eds.) :
In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings,
Vol. 3630
CEUR-WS.org. pp. 430-441
[PDF, 953kB]

Abstract
The selection of useful, informative, and meaningful features is a key prerequisite for the successful application of machine learning in practice, especially in knowledge-intense domains like decision support. Here, the task of feature selection, or ranking features by importance, can, in principle, be solved automatically in a data-driven way but also supported by expert knowledge. Besides, one may of course, conceive a combined approach, in which a learning algorithm closely interacts with a human expert. In any case, finding an optimal approach requires a basic understanding of human capabilities in judging the importance of features compared to those of a learning algorithm. Hereto, we conducted a case study in the medical domain, comparing feature rankings based on human judgment to rankings automatically derived from data. The quality of a ranking is determined by the performance of a decision list processing features in the order specified by the ranking, more specifically by so-called probabilistic scoring systems.
Item Type: | Conference or Workshop Item (Paper) |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
URN: | urn:nbn:de:bvb:19-epub-109222-8 |
Language: | English |
Item ID: | 109222 |
Date Deposited: | 13. Feb 2024 15:38 |
Last Modified: | 14. Oct 2024 11:57 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 438445824 |