3 papers
cs.AI2025
Leveraging Association Rules for Better Predictions and Better Explanations
Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis +2
We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data.…
cs.LG2025
A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees
Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis +2
We present a new approach for distilling boosted trees into decision trees, in the objective of generating an ML model offering an acceptable compromise in terms of predictive perf…
cs.AI2024
PyCSP3: Modeling Combinatorial Constrained Problems in Python
Christophe Lecoutre, Nicolas Szczepanski
In this document, we introduce PyCSP, a Python library that allows us to write models of combinatorial constrained problems in a declarative manner. Currently, with PyCSP, yo…