Abstract Submission

6680. Exploiting Sparse Decision Trees To Learn Minimal Rules

Contributed abstract in session TE-1: Optimization and Artificial Intelligence I, stream Optimization and Artificial Intelligence.

Thursday, 16:50 - 18:30
Room: Fermat

Authors (first author is the speaker)

1. Tommaso Aldinucci
Dipartimento di Ingegneria dell'Informazione, Università Degli Studi di Firenze


Decision tree algorithms have been one of the most famous and used algorithms in machine learning since the early 1980’s.
Given their intrinsic explainability, nowadays they are widely used in contexts where transparency is desired.
In this work we propose a new heuristic strategy to extract minimal rules from decision trees. At first the algorithm builds a sparse structure then uses a local optimizer to improve the quality of the tree.
We finally show that the rule set extracted from the final model can be often simplified using elementary rules of boolean logic.


Status: accepted

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