Name
Title | ||
|---|---|---|
A new multi-objective wrapper method for feature selection - Accuracy and stability analysis for BCI. |
Abstract | ||
|---|---|---|
Feature selection is an important step in building classifiers for high-dimensional data problems, such as EEG classification for BCI applications. This paper proposes a new wrapper method for feature selection, based on a multi-objective evolutionary algorithm, where the representation of the individuals or potential solutions, along with the breeding operators and objective functions, have been carefully designed to select a small subset of features that has good generalization capability, trying to avoid the over-fitting problems that wrapper methods usually suffer. A novel feature ranking procedure is also proposed in order to analyze the stability of the proposed wrapper method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2019.01.017 | Neurocomputing |
Keywords | Field | DocType |
BCI,EEG,Motor imagery,Feature selection,Multi-objective problem,Evolutionary algorithm,Classification,Stability,Ensemble | Pattern recognition,Feature selection,Eeg classification,Evolutionary algorithm,Classification scheme,Feature ranking,Brain–computer interface,Artificial intelligence,Operator (computer programming),Mathematics,Machine learning,Motor imagery | Journal |
Volume | ISSN | Citations |
333 | 0925-2312 | 6 |
PageRank | References | Authors |
0.44 | 34 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús González | 1 | 604 | 44.40 |
| J. Ortega | 2 | 940 | 73.05 |
| M. Damas | 3 | 387 | 33.04 |
| P. Martín-Smith | 4 | 29 | 4.04 |
| John Q. Gan | 5 | 18 | 4.87 |