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