Learning fuzzy decision trees using integer programming
By Jason Rhuggenaath, Yingqian Zhang, Alp Eren Akçay, Uzay Kaymak, Sicco Verwer. 2018
11 citations recorded by the source corpus.
A popular method in machine learning for supervised classification is a decision tree. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver. We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data.
Subfields: Artificial Intelligence, Statistics and Probability
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
- Carlos Eduardo Pedreira
- Vinícius G. Costa
- Yingqian Zhang
- Adriana Felicia Gabor
- Cor A. J. Hurkens
- Murat Firat
- Guillaume Crognier
- Dzung T. Phan
In Statistics and Probability
- Lam M. Nguyen
- Carlos Eduardo Pedreira
- Jayant R. Kalagnanam
- Sicco Verwer
- Adriana Felicia Gabor
- Cor A. J. Hurkens
- Murat Firat
- Vinícius G. Costa
Citations in the corpus
- Learning Optimal Classification Trees Using a Binary Linear Program Formulation (2019, background): “(Rhuggenaath et al. 2018) build a mixed integer linear program to learn fuzzy decision trees, where split decisions on the internal nodes of a tree are not crisp.”
- Recent advances in decision trees: an updated survey (2022, methodology): “…several discrete choices, the formulations themselves are usually classified as either MIO ( Verwer and Zhang 2017; Bertsimas and Dunn 2017; Rhuggenaath et al. 2018; Aghaei et al. 2019; Firat et al. 2020; Günlük et al. 2021) or purely binary integer programming (BIP; Verwer and Zhang 2019).”
- Constructing classification trees using column generation (2018, background): “In a future study, this sampling procedure can be tested with other ideas of information collection, e.g., Rhuggenaath et al. (2018).”
- A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (2020, methodology): “The discrete nature of decisions involved in training a decision tree has inspired researchers in the field of Operations Research to encode the process using a mixed-integer programming (MIP) framework [5, 12, 19, 22, 23].”