FUZZ-IEEE2023: Call for Papers

FUZZ-IEEE2023: Call for Papers by Feb 15

The Annual IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) is one of the premier international conferences in the field of fuzzy sets and systems.

Special Session Abstract

This special session aims to provide a forum for researchers in the fuzzy machine learning field to share the latest advantages in the integration of fuzzy techniques and machine learning methods, especially how fuzzy techniques help machine learning methods in handling uncertainty, enhancing interpretability and improving robustness.

Topics

In this session, we aim to study the theories, models, algorithms and application of fuzzy machine learning and provide a platform to host novel ideas based on the integration of machine learning algorithms and fuzzy sets, fuzzy logic and fuzzy systems. The main topics of this special session include, but are not limited to, the following:

• Fuzzy technique-based feature selection and extraction;
• Fuzzy rule-based knowledge representation in machine learning;
• Fuzzy classification, fuzzy regression, and fuzzy clustering;
• Fuzzy transfer learning;
• Fuzzy concept drift;
• Fuzzy neural networks to modelling complex problems;
• Fuzzy support vector machine;
• Fuzzy decision trees;
• Fuzzy modelling for handling uncertainties in machine learning models;
• Methods to improve models' interpretability using fuzzy techniques;
• Methods to enhance models' robustness using fuzzy techniques;
• Granular clustering, modelling and control;
• Fuzzy techniques for aggregation, combination and information fusion in machine learning models;
• Fuzzy machine learning based decision support;
• Application in transport, ICT, healthcare, business intelligence and more.

Submit your paper here.

Organisers

• Dr. Hua Zuo, University of Technology Sydney, hua.zuo@uts.edu.au
• Dr. Zhen Fang, University of Technology Sydney, zhen.fang@uts.edu.au
• Prof. Witold Pedrycz, University of Alberta, Canada, wpedrycz@ualberta.ca
• Prof. Guangquan Zhang, University of Technology Sydney, guangquan.zhang@uts.edu
• Dist. Prof. Jie Lu, University of Technology Sydney, jie.lu@uts.edu.au