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Jeudi 25 Septembre
| Heure: |
10:30 - 12:00 |
| Lieu: |
Salle B107, bâtiment B, Université de Villetaneuse |
| Résumé: |
New formulations and algorithms for the optimal classification tree problem |
| Description: |
Zacharie Ales Classification trees are models that provide highly interpretable classifiers but generally do not perform as well as neural networks. To obtain classifiers that are both interpretable and performant, we consider the problem of computing an optimal classification tree for a given data set. To address this problem, we first define new mathematical formulations in the form of mixed integer linear programs (MILP) and demonstrate that they are stronger and more efficient than state-of-the-art MILPs. To handle larger datasets, we then define iterative algorithms based on a data partition that is refined throughout the iterations. |
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