Name
Title | ||
|---|---|---|
FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptions |
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0888-613X(02)00080-4 | International Journal of Approximate Reasoning |
Keywords | Field | DocType |
Clustering,Data mining,Fuzzy rules,Inductive learning,Interpretability,Machine learning | Fuzzy clustering,Data mining,Inductive bias,Neuro-fuzzy,Multi-task learning,Fuzzy classification,Defuzzification,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 2 | 0888-613X |
Citations | PageRank | References |
21 | 1.72 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Drobics | 1 | 169 | 15.52 |
| Ulrich Bodenhofer | 2 | 705 | 68.02 |
| Erich Peter Klement | 3 | 989 | 128.89 |