Embedded Feature Construction in Fuzzy Decision Tree Induction for High Energy Physics Classification

Noelie Cherrier (1, 2) , Jean-Philippe Poli (1) , Maxime Defurne (2) , Franck Sabatie (2)

1 LI3A (CEA, LIST) - Intelligence Artificielle et Apprentissage Automatique (CEA, LIST)
2 IRFU - Institut de Recherches sur les lois Fondamentales de l'Univers

Fuzzy decision trees have been successfully applied in numerous domains. The popularity of these models comes notably from their interpretability, namely the ability of humans to understand them. However, on the contrary to neural networks, the induction of such models does not include a generation of their own feature space. In this work, the embedding of feature construction in fuzzy decision tree induction algorithms is studied, so that they can create new input features, without affecting the overall interpretability of the model. This method is successfully applied to a classification problem in high-energy physics to study the benefits of having constructed features in fuzzy decision tree on the classification scores, allowing them to have their own interpretable representation of the data.

Mots clés: machine learning; artificial intelligence; fuzzy logic; online learning; Fuzzy decision tree; decision tree; interpretability; Trustworthy Artificial intelligence; neural network; feature construction; classification; high energy physics; classification score; interpretable data representation; Cybernetics; Regression Tree; Feature Space; Model Interpretation; Fuzzy Algorithm; Learning Algorithms; Gene Regulatory Networks; Adaptive Algorithm; Information Gain; Root Node; Membership Function; Tree Nodes; Scientific Experiments; Candidate Features; Raw Features; Interpretation Of Features; Particle Momentum; Construction Field; genetic programming

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Logique en informatique [cs.LO]; Physique des Hautes Energies - Théorie [hep-th]; Machine Learning [stat.ML]

Noelie Cherrier, Jean-Philippe Poli, Maxime Defurne, Franck Sabatie. Embedded Feature Construction in Fuzzy Decision Tree Induction for High Energy Physics Classification. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2020, Toronto, Canada. pp.615-622, ⟨10.1109/SMC42975.2020.9283103⟩. ⟨cea-04564515⟩