Name
Title | ||
|---|---|---|
On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection |
Abstract | ||
|---|---|---|
The k-nearest neighbors classifier is a widely used classification method that has proven to be very effective in supervised learning tasks. In this paper, a fuzzy rough set method for prototype selection, focused on optimizing the behavior of this classifier, is presented. The hybridization with an evolutionary feature selection method is considered to further improve its performance, obtaining a competent data reduction algorithm for the 1-nearest neighbors classifier. This hybridization is performed in the training phase, by using the solution of each preprocessing technique as the starting condition of the other one, within a cycle. The results of the experimental study, which have been contrasted through nonparametric statistical tests, show that the new hybrid approach obtains very promising results with respect to classification accuracy and reduction of the size of the training set. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s00500-012-0888-3 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
data reduction,prototype selection,evolutionary algorithms,fuzzy rough sets,nearest neighbor,feature selection | k-nearest neighbors algorithm,Interpretability,Pattern recognition,Feature selection,Evolutionary algorithm,Computer science,Supervised learning,Preprocessor,Artificial intelligence,Classifier (linguistics),Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
17 | 2 | 1433-7479 |
Citations | PageRank | References |
15 | 0.52 | 68 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joaquín Derrac | 1 | 2552 | 64.42 |
| Nele Verbiest | 2 | 232 | 12.23 |
| Salvador García | 3 | 4151 | 118.45 |
| Chris Cornelis | 4 | 2116 | 113.39 |
| Francisco Herrera | 5 | 27391 | 1168.49 |