Abstract
AbstractClassification 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.
Publisher
Springer Science and Business Media LLC
Subject
Discrete Mathematics and Combinatorics,Statistics and Probability,Management Science and Operations Research,Information Systems and Management,Modeling and Simulation
Reference196 articles.
1. Aghaei S, Azizi MJ, Vayanos P (2019) Learning optimal and fair decision trees for non-discriminative decision-making. Proc AAAI Conf Artif Intell 33:1418–1426
2. Aghaei S, Gomez A, Vayanos P (2020) Learning optimal classification trees: strong max-flow formulations. arXiv:2002.09142
3. Aglin G, Nijssen S, Schaus P (2020) Learning optimal decision trees using caching branch-and-bound search. In: Thirty-Fourth AAAI Conference on Artificial Intelligence
4. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Prentice Hall, New Jersey
5. Altmann A, Toloşi L, Sander O, Lengauer T (2010) Permutation importance: a corrected feature importance measure. Bioinformatics 26(10):1340–1347
Cited by
66 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献