Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain

Hanselle, Jonas ; Kornowicz, Jaroslaw; Heid, Stefan; Thommes, Kirsten und Hüllermeier, Eyke (Oktober 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 (Hrsg.): In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Bd. 3630 CEUR-WS.org. S. 430-441

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 438445824