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Hanselle, Jonas ORCID logoORCID: https://orcid.org/0000-0002-1231-4985; Kornowicz, Jaroslaw; Heid, Stefan; Thommes, Kirsten ORCID logoORCID: https://orcid.org/0000-0002-8057-7162 and Hüllermeier, Eyke ORCID logoORCID: 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 and Wichmann, Johannes (eds.) : In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Vol. 3630 CEUR-WS.org. pp. 430-441

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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.

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