Project

Data reduction based on fuzzy rough sets

Code
01J03211
Duration
01 May 2010 → 30 September 2015
Funding
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Artificial intelligence
  • Social sciences
    • Cognitive science and intelligent systems
Keywords
machine learning fuzzy rough sets feature selection classification instance selection regression
 
Project description

As datasets are getting more and more high-dimensional, there is an emerging need for data reduction techniques. This project focuses both on feature selection that selects relevant features and instance selection that removes irrelevant or noisy instances. More specifically, we work on fuzzy rough data reduction, data reduction for imbalanced datasets and robust data reduction.