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MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making
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DOI：10.1007/978-3-032-08327-2_6.png)
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Abstract
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A scoring list is a sequence of simple decision models, where features are incrementally evaluated and scores of satisfied features are summed to be used for threshold-based decisions or for calculating class probabilities. In this paper, we introduce a new multi-class variant and compare it against previously introduced binary classification variants for incremental decisions, as well as multi-class variants for classical decision-making using all features. Furthermore, we introduce a new multi-class dataset to assess collaborative human-machine decision-making, which is suitable for user studies with non-expert participants. We demonstrate the usefulness of our approach by evaluating predictive performance and compared to the performance of participants without AI help.
Keywords:
machine learning
decision support
scoring systems
user study
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