A Bayesian Approach to Learning Scoring Systems

13 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Optimized Risk Scores (2016, methodology): “, assess optimality, and accomodate operational constraints. In addition, we propose new techniques to generate feasible solutions and reduce data-related computation that may improve the approach of Ertekin and Rudin (2015). Our proposed methodology can be used to solve a broad range of machine lea”
A Provable Algorithm for Learning Interpretable Scoring Systems (2018, background): “The state-of-the-art methods [27, 7, 16, 30, 28] are reported to be accurate, but an obvious drawback is that their output, the learned scores, apply to real-valued data (if the input data were real).”
The fused lasso penalty for learning interpretable medical scoring systems (2017, background): “The state-of-the-art methods of [6] and [25] are reported to be efficient, but an obvious drawback is that their output, the learned scores, apply to real-valued data (if the input data were real).”