Name
Abstract | ||
|---|---|---|
In this paper, a high-dimensional textural heterogenous dataset is evaluated. This problem should be studied with specific techniques or a solution for decreasing dimensionality should be applied in order to improve the classification results. Thus, this problem is tackled by means of three differente techniques: an specific technique such as Multiple Kernel Learning, and two different feature selection techniques such as Support Vector Machines-Recursive Feature Elimination and a Genetic Algorithm-based approaches. We found that the best technique is Support Vector Machines-Recursive Feature Elimination, with a AUROC score of 92,45%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38679-4_42 | IWANN (1) |
Keywords | Field | DocType |
specific technique,best technique,multiple kernel learning,auroc score,texture classification,genetic algorithm-based approach,differente technique,different feature selection technique,kernel-based technique,classification result,support vector machines-recursive feature,genetic algorithms,support vector machines | Graph kernel,Data mining,Radial basis function kernel,Computer science,Polynomial kernel,Artificial intelligence,Feature vector,Pattern recognition,Kernel embedding of distributions,Multiple kernel learning,Support vector machine,Kernel method,Machine learning | Conference |
Volume | ISSN | Citations |
7902 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Fernandez-Lozano | 1 | 20 | 8.32 |
| José A. Seoane | 2 | 76 | 9.29 |
| Marcos Gestal | 3 | 43 | 8.46 |
| Tom R. Gaunt | 4 | 61 | 10.36 |
| Colin Campbell | 5 | 568 | 41.10 |