Name
Abstract | ||
|---|---|---|
As the complexity of data increases, so does the importance of powerful representations, such as relational and logical representations, as well as the need for machine learning methods that can learn predictive models in such representations. A characteristic of these representations is that they give rise to a huge number of features to be considered, thus drastically increasing the difficulty of learning in terms of computational complexity and the curse of dimensionality. Despite this, methods for ranking features in this context, i.e., estimating their importance are practically non-existent. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2022.109254 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Relational learning,Tree ensembles,Feature ranking,Propositionalization | Journal | 251 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matej Petkovic | 1 | 0 | 0.34 |
| Michelangelo Ceci | 2 | 737 | 86.28 |
| Gianvito Pio | 3 | 0 | 0.34 |
| Blaz Skrlj | 4 | 3 | 5.60 |
| Kristian Kersting | 5 | 1932 | 154.03 |
| Sago Dzeroski | 6 | 0 | 0.34 |