Abstract
In this chapter, methodologies for more robustness and transparency in
evolving fuzzy systems will be demonstrated. After outlining the evolving
fuzzy modelling approaches FLEXFIS (=FLEXible Fuzzy Inference Systems) for fuzzy regression models
and FLEXFIS-Class (=FLEXible Fuzzy Inference Systems for Classification) for fuzzy classification models,
robustness improvement strategies during the incremental learning phase will be explained, including
regularization issues for overcoming instabilities in the parameter estimation process,
overcoming the unlearning effect, dealing with drifts in data streams, ensuring a better extrapolation behavior
and adaptive local error bars serving as local confidence regions surrounding the evolved fuzzy models.
The chapter will be concluded with evaluation results on high-dimensional real-world data sets, where
the impact of the new methodologies will be presented.
| Original language | English |
|---|---|
| Title of host publication | Evolving Intelligent Systems: Methodologies and Applications |
| Editors | Plamen Angelov, Dimitar Filev, Nikola Kasabov |
| Publisher | John Wiley & Sons |
| Pages | 87-126 |
| Number of pages | 45 |
| Publication status | Published - Apr 2010 |
Fields of science
- 101 Mathematics
- 101004 Biomathematics
- 101027 Dynamical systems
- 101013 Mathematical logic
- 101028 Mathematical modelling
- 101014 Numerical mathematics
- 101020 Technical mathematics
- 101024 Probability theory
- 102001 Artificial intelligence
- 102003 Image processing
- 102009 Computer simulation
- 102019 Machine learning
- 102023 Supercomputing
- 202027 Mechatronics
- 206001 Biomedical engineering
- 206003 Medical physics
- 102035 Data science