Learning fuzzy decision trees using integer programming

Conference paper (2018)

Authors

Jason. S. Rhuggenaath Eindhoven University of Technology
Yingqian Zhang Eindhoven University of Technology
Alp Akcay Eindhoven University of Technology
U Kaymak Eindhoven University of Technology
S.E. Verwer Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Optimization Machine learning Fuzzy logic Mathematical programming Fuzzy systems
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Published Date

01-02-2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

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.

Files

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