From the 1 of 9 linked papers with an AI index.
4 papers · 1 filter
Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
Wenhao Chi, Å. Ä°lker Birbil
The paper introduces an interpretable risk scoring system that directly maximizes decision net benefit by formulating the problem as a sparse integer linear program, and shows it m…
Output-Constrained Decision Trees
Hüseyin Tunç, DoÄanay Ãzese, Å. Ä°lker Birbil +3
Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This…
Generating Samples to Probe Trained Models
Eren Mehmet Kıral, NurÅen Aydın, Å. Ä°lker Birbil
There is a growing need for investigating how machine learning models operate. With this work, we aim to understand trained machine learning models by questioning their data prefer…
Rule Generation for Classification: Scalability, Interpretability, and Fairness
Tabea E. Röber, Adia C. Lumadjeng, M. Hakan Akyüz +1
We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalabl…