Standard

Learning fuzzy decision trees using integer programming. / Rhuggenaath, Jason. S.; Zhang, Yingqian; Akcay, Alp; Kaymak, Uzay; Verwer, Sicco.

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway : IEEE, 2018. p. 1-8.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Rhuggenaath, JS, Zhang, Y, Akcay, A, Kaymak, U & Verwer, S 2018, Learning fuzzy decision trees using integer programming. in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, Piscataway, pp. 1-8, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/FUZZ-IEEE.2018.8491636

APA

Rhuggenaath, J. S., Zhang, Y., Akcay, A., Kaymak, U., & Verwer, S. (2018). Learning fuzzy decision trees using integer programming. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). Piscataway: IEEE. https://doi.org/10.1109/FUZZ-IEEE.2018.8491636

Vancouver

Rhuggenaath JS, Zhang Y, Akcay A, Kaymak U, Verwer S. Learning fuzzy decision trees using integer programming. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway: IEEE. 2018. p. 1-8 https://doi.org/10.1109/FUZZ-IEEE.2018.8491636

Author

Rhuggenaath, Jason. S. ; Zhang, Yingqian ; Akcay, Alp ; Kaymak, Uzay ; Verwer, Sicco. / Learning fuzzy decision trees using integer programming. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway : IEEE, 2018. pp. 1-8

BibTeX

@inproceedings{6b9b7dbb7475432c9ff47b66203f5457,
title = "Learning fuzzy decision trees using integer programming",
abstract = "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.",
keywords = "Fuzzy systems, Machine learning, Fuzzy logic, Optimization, Mathematical programming",
author = "Rhuggenaath, {Jason. S.} and Yingqian Zhang and Alp Akcay and Uzay Kaymak and Sicco Verwer",
year = "2018",
month = "2",
day = "1",
doi = "10.1109/FUZZ-IEEE.2018.8491636",
language = "English",
isbn = "978-1-5090-6021-4",
pages = "1--8",
booktitle = "2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Learning fuzzy decision trees using integer programming

AU - Rhuggenaath, Jason. S.

AU - Zhang, Yingqian

AU - Akcay, Alp

AU - Kaymak, Uzay

AU - Verwer, Sicco

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

KW - Fuzzy systems

KW - Machine learning

KW - Fuzzy logic

KW - Optimization

KW - Mathematical programming

U2 - 10.1109/FUZZ-IEEE.2018.8491636

DO - 10.1109/FUZZ-IEEE.2018.8491636

M3 - Conference contribution

SN - 978-1-5090-6021-4

SP - 1

EP - 8

BT - 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

PB - IEEE

CY - Piscataway

ER -

ID: 47448422