Learning fuzzy decision trees using integer programming

Jason. S. Rhuggenaath, Yingqian Zhang, Alp Akcay, Uzay Kaymak, Sicco Verwer

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)


A popular method in machine learning for super-vised 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.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Place of PublicationPiscataway
Number of pages8
ISBN (Electronic)978-1-5090-6020-7
ISBN (Print)978-1-5090-6021-4
Publication statusPublished - 1 Feb 2018
Event2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018


Conference2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
CityRio de Janeiro


  • Fuzzy systems
  • Machine learning
  • Fuzzy logic
  • Optimization
  • Mathematical programming


Dive into the research topics of 'Learning fuzzy decision trees using integer programming'. Together they form a unique fingerprint.

Cite this