Name
Title | ||
|---|---|---|
Performance Evaluation of Prototype Selection Algorithms for Nearest Neighbor Classification |
Abstract | ||
|---|---|---|
Prototype selection is primarily effective in improving the classification performance of Nearest Neighbor (NN) classifier and also partially in reducing its storage and computational requirements.This paper reviews some prototype selection algorithms for NN classification and experimentally evaluates their performance using a number of real data sets.Finally, new approaches based on combining the NN and the Nearest Centroid Neighbor (NCN) of a sample [3] are also introduced. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/SIBGRAPI.2001.963036 | SIBGRAPI |
Keywords | Field | DocType |
computational requirement,prototype selection algorithm,performance evaluation,nearest neighbor classification,nearest centroid neighbor,nearest neighbor,nn classification,prototype selection,prototype selection algorithms,new approach,classification performance,pattern recognition,prototypes,neural networks,databases,pattern analysis,classification algorithms | Data mining,Data set,Best bin first,Computer science,Nearest-neighbor chain algorithm,Artificial intelligence,Large margin nearest neighbor,Classifier (linguistics),k-nearest neighbors algorithm,Pattern recognition,Algorithm,Cover tree,Centroid | Conference |
ISBN | Citations | PageRank |
0-7695-1330-1 | 5 | 0.54 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Salvador Sánchez | 1 | 565 | 31.62 |
| R Barandela | 2 | 558 | 23.46 |
| A. I. Marqués | 3 | 209 | 10.40 |
| Roberto Alejo | 4 | 9 | 2.70 |