Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics
This work addresses the need for interpretable feature construction in experimental physics, offering a domain-specific solution that is validated by experts, though it is incremental as it builds on existing Genetic Programming techniques.
The paper tackled the problem of automatic and interpretable feature construction for particle collision classification in high-energy physics by combining Genetic Programming with dimensional consistency enforced via grammars, resulting in constructed features that brought significant gains in classification accuracy on three physics datasets.
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 particular, to be applicable to physics, dimensional consistency is enforced using grammars. Results of experiments on three physics datasets show that the constructed features can bring a significant gain to the classification accuracy. To the best of our knowledge, it is the first time a method is proposed for interpretable feature construction with units of measurement, and that experts in high-energy physics validate the overall approach as well as the interpretability of the built features.