Interval Coded Scoring Index with Interaction Effects - A Sensitivity Study

By Lieven Billiet, S. Van Huffel, Vanya Van Belle. 2016

4 citations recorded by the source corpus.

Scoring systems have been used since long in medical practice, but often they are based on experience rather than a structural approach. In literature, the interval coded scoring index (ICS) has been introduced as an alternative. It derives a scoring system from data using optimization techniques. This work discusses an extension, ICS*, that takes variable interactions into account. Furthermore, a study is performed to give insight into the new modelâ??s sensitivity to noise, the size of the data set and the number of non-informative variables. The study shows interactions can mostly be discovered robustly, even in the presence of noise and spurious variables. A final validation on two UCI data sets further indicates the quality of the approach.

Subfields: Statistics and Probability, Signal Processing

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Citations in the corpus

Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, background): “Furthermore, it explicitly handles the concepts of main effects and variable interactions (Billiet et al., 2016).”
Learning Optimized Risk Scores (2016, methodology): “These include methods to learn: scoring systems for decision-making (Carrizosa et al., 2016; Van Belle et al., 2013; Billiet et al., 2016, 2017; Sokolovska et al., 2017); sparse Boolean classifiers (e.g., Chevaleyre et al., 2013; Malioutov and Varshney, 2013; Goh and Rudin, 2014; Wang et al.,…”