Dealing with Imbalanced and Weakly Labeled Data in Machine Learning using Fuzzy and Rough Set Methods

01 October 2014 → 30 September 2018
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Artificial intelligence
  • Social sciences
    • Cognitive science and intelligent systems
fuzziness and uncertainty modelling machine learning
Project description

The goal of this project is to tackle two important and challenging problems in machine learning, namely learning from imbalanced and weakly labeled data, using the hybridization of fuzzy sets and rough sets.

A thorough study and explicit enhancement of fuzzy-rough methodologies will allow for the construction of robust new solutions tailored specifically to the problems stated above.