Abstract
This paper is concerned with FS-FOIL - an extension of Quinlan's First-Order Inductive Learning
Method (FOIL). In contrast to the classical FOIL algorithm, FS-FOIL uses fuzzy predicates and,
thereby, allows to deal not only with categorical variables, but also with numerical ones, without
the need to draw sharp boundaries. This method is described in full detail along with discussions
how it can be applied in different traditional application scenarios - classification, fuzzy
modeling, and clustering. We provide examples of all three types of applications in order to
illustrate the efficiency, robustness, and wide applicability of the FS-FOIL method.
| Original language | English |
|---|---|
| Pages (from-to) | 131-152 |
| Number of pages | 22 |
| Journal | International Journal of Approximate Reasoning |
| Volume | 32 |
| Issue number | 2-3 |
| Publication status | Published - Feb 2003 |
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