Name
Abstract | ||
|---|---|---|
The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has also been used successfully for regression problems where the purpose is to predict a continuous numeric label. However, some alternative neighborhood definitions, such as the surrounding neighborhood, have considered that the neighbors should fulfill not only the proximity property, but also a spatial location criterion. In this paper, we explore the use of the k-nearest centroid neighbor rule, which is based on the concept of surrounding neighborhood, for regression problems. Two support vector regression models were executed as reference. Experimentation over a wide collection of real-world data sets and using fifteen odd different values of k demonstrates that the regression algorithm based on the surrounding neighborhood significantly outperforms the traditional k-nearest neighborhood method and also a support vector regression model with a RBF kernel. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10044-018-0706-3 | Pattern Anal. Appl. |
Keywords | Field | DocType |
Nearest neighborhood,Regression analysis,Surrounding neighborhood,Symmetry criterion | Decision rule,Data set,Pattern recognition,Regression,Radial basis function kernel,Regression analysis,Support vector machine,Metric (mathematics),Artificial intelligence,Mathematics,Centroid | Journal |
Volume | Issue | ISSN |
21 | 4 | 1433-7541 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente García | 1 | 78 | 6.37 |
| J. Salvador Sánchez | 2 | 139 | 14.01 |
| A. I. Marqués | 3 | 209 | 10.40 |
| R. Martínez-Peláez | 4 | 0 | 0.34 |