Mathematical optimization in classification and regression trees
By Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales. 2021
137 citations recorded by the source corpus.
Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as cost-sensitivity, explainability, and fairness, and to deal with complex data, such as functional data.
Subfields: Artificial Intelligence, Statistical Learning Theory, Mathematical Optimization
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ryan McNellis
- Kris Johnson Ferreira
- Ali Aouad
- Carlos Eduardo Pedreira
- Adam N. Elmachtoub
- Vinícius G. Costa
- Stefan Szeider
- André Schidler
In Statistical Learning Theory
In Mathematical Optimization
Citations in the corpus
- Recent advances in decision trees: an updated survey (2022, background): “More recent surveys do exist, but they focus on specific tree approaches, like fuzzy trees (Sosnowski and Gadomer 2019) and mathematical programming trees (Carrizosa et al. 2021), therefore not covering the field as a whole.”
- Market Segmentation Trees (2019, background): “(2001), as well as more recent surveys on their interpretability Rudin (2019) and optimization algorithms (Carrizosa et al. 2021).”