Name
Abstract | ||
|---|---|---|
This work presents an evolutionary approach to modify the voting system of the k-Nearest Neighbours (kNN). The main novelty of this article lies on the optimization process of voting regardless of the distance of every neighbour. The calculated real-valued vector through the evolutionary process can be seen as the relative contribution of every neighbour to select the label of an unclassified example. We have tested our approach on 30 datasets of the UCI repository and results have been compared with those obtained from other 6 variants of the kNN predictor, resulting in a realistic improvement statistically supported. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-07617-1_27 | HAIS |
Keywords | Field | DocType |
evolutionary computation,fuzzy knn,knn voting | Nearest neighbour,Voting,Pattern recognition,Computer science,Evolutionary computation,Artificial intelligence,Fuzzy knn,Novelty,Machine learning | Conference |
Volume | ISSN | Citations |
8480 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Garcia-Gutierrez | 1 | 35 | 9.33 |
| Daniel Mateos-Garcia | 2 | 17 | 3.75 |
| José Cristóbal Riquelme Santos | 3 | 318 | 42.86 |