Name
Abstract | ||
|---|---|---|
The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact set of relevant, highly predictive features with as little as 60% of the time required by the original wrapper. |
Year | Venue | Field |
|---|---|---|
2015 | ESANN | Sequential data,Search algorithm,Pattern recognition,Feature selection,Computer science,Feature (computer vision),Compact space,Heuristics,Artificial intelligence,Feature learning,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Bacciu | 1 | 222 | 35.96 |
| Filippo Benedetti | 2 | 3 | 1.28 |
| Alessio Micheli | 3 | 713 | 60.24 |