• Jason. S. Rhuggenaath
  • Yingqian Zhang
  • Alp Akcay
  • Uzay Kaymak
  • Sicco Verwer
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
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-5090-6020-7
ISBN (Print)978-1-5090-6021-4
DOIs
Publication statusPublished - 1 Feb 2018
Event2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

    Research areas

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

ID: 47448422