Name
Title | ||
|---|---|---|
Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification. |
Abstract | ||
|---|---|---|
•Novel embedded feature selection approach for SVM for imbalanced data sets.•Optimization is performed via Quasi-Newton and Armijo Search.•Best classification performance is achieved in experiments on benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2018.02.051 | Applied Soft Computing |
Keywords | Field | DocType |
Feature selection,Support Vector Data Description,Cost-sensitive learning,Embedded approaches,Imbalanced data classification | Pattern recognition,Feature selection,Support vector machine,Cardinality,Curse of dimensionality,Line search,Artificial intelligence,Scaling,Gaussian function,Mathematics,Machine learning,Data description | Journal |
Volume | Issue | ISSN |
67 | C | 1568-4946 |
Citations | PageRank | References |
14 | 0.54 | 29 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastián Maldonado | 1 | 508 | 32.45 |
| Julio López | 2 | 124 | 13.49 |