Abstract
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.
Original language | English |
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Title of host publication | 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 312-319 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-5090-6020-7 |
DOIs | |
Publication status | Published - 12 Oct 2018 |
Event | 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 |
Conference
Conference | 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 |
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Abbreviated title | FUZZ 2018 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |
Keywords
- Fuzzy logic
- Fuzzy systems
- Machine learning
- Mathematical programming
- Optimization