Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier,
"Probabilistic Scoring Lists for Interpretable Machine Learning"
, in A. Bifet, A. C. Lorena, R. P. Ribeiro, J. Gama, and P. H. Abreu: Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings, Serie Lecture Notes in Computer Science (LNCS), Vol. 14276, Springer, Seite(n) 189-203, 2023
Original Titel:
Probabilistic Scoring Lists for Interpretable Machine Learning
Sprache des Titels:
Englisch
Original Buchtitel:
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings
Original Kurzfassung:
A scoring system is a simple decision model that checks a set of features, adds a certain number of points to a total score for each feature that is satisfied, and finally makes a decision by comparing the total score to a threshold. Scoring systems have a long history of active use in safety-critical domains such as healthcare and justice, where they provide guidance for making objective and accurate decisions. Given their genuine interpretability, the idea of learning scoring systems from data is obviously appealing from the perspective of explainable AI. In this paper, we propose a practically motivated extension of scoring systems called probabilistic scoring lists (PSL), as well as a method for learning PSLs from data. Instead of making a deterministic decision, a PSL represents uncertainty in the form of probability distributions. Moreover, in the spirit of decision lists, a PSL evaluates features one by one and stops as soon as a decision can be made with enough confidence. To evaluate our approach, we conduct a case study in the medical domain.
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