Name
Abstract | ||
|---|---|---|
This paper presents a supervised feature selection method applied to regression problems. The selection method uses a Dissimilarity matrix originally developed for classification problems, whose applicability is extended here to regression and built using the conditional mutual information between features with respect to a continuous relevant variable that represents the regression function. Applying an agglomerative hierarchical clustering technique, the algorithm selects a subset of the original set of features. The proposed technique is compared with other three methods. Experiments on four data-sets of different nature are presented to show the importance of the features selected from the point of view of the regression estimation error (using Support Vector Regression) considering the Root Mean Squared Error (RMSE). |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21257-4_28 | IbPRIA |
Keywords | Field | DocType |
support vector regression,regression estimation error,supervised feature selection method,selection method,regression function,dissimilarity matrix,regression task,proposed technique,agglomerative hierarchical clustering technique,classification problem,root mean squared error,conditional mutual information,regression,feature selection | Data mining,init,Regression,Feature selection,Pattern recognition,Computer science,Regression analysis,Nonparametric regression,Support vector machine,Mean squared error,Artificial intelligence,Conditional mutual information | Conference |
Volume | ISSN | Citations |
6669 | 0302-9743 | 4 |
PageRank | References | Authors |
0.44 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Latorre Carmona | 1 | 23 | 6.55 |
| José M. Sotoca | 2 | 13 | 2.03 |
| Filiberto Pla | 3 | 557 | 60.06 |
| Frederick K. H. Phoa | 4 | 15 | 6.18 |
| José M. Bioucas-Dias | 5 | 3565 | 173.67 |