- author = "Noelie Cherrier and Jean-Philippe Poli and Maxime Defurne and Franck Sabatie",
- title = "Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "1650--1658",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, construction, grammar-guided genetic programming, high-energy physics, interpretability",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8789937",
- size = "9 pages",
- abstract = "A good feature representation is a determinant factor to achieve high performance for many machine learning algorithms in terms of classification. This is especially true for techniques that do not build complex internal representations of data (e.g. decision trees, in contrast to deep neural networks). To transform the feature space, feature construction techniques build new high-level features from the original ones. Among these techniques, Genetic Programming is a good candidate to provide interpretable features required for data analysis in high energy physics. Classically, original features or higher-level features based on physics first principles are used as inputs for training. However, physicists would benefit from an automatic and interpretable feature construction for the classification of particle collision events.Our main contribution consists in combining different aspects of Genetic Programming and applying them to feature construction for experimental physics. In parti",
-
notes = "also known as \cite{8789937},
IEEE Catalog Number: CFP19ICE-ART",
