Name
Abstract | ||
|---|---|---|
How to select the prototypes for classification in the dissimilarity space remains an open and interesting problem. Especially, achieving scalability of the methods is desirable due to enormous amounts of information arising in many fields. In this paper we pose the question: are genetic algorithms good for scalable prototype selection? We propose two methods based on genetic algorithms, one supervised and the other unsupervised, whose analyses provide an answer to the question. Results on dissimilarity datasets show the effectiveness of the proposals. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-44415-3_35 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
dissimilarity space,scalable prototype selection,genetic algorithm | Data mining,Computer science,Artificial intelligence,Machine learning,Genetic algorithm,Scalability | Conference |
Volume | ISSN | Citations |
8621 | 0302-9743 | 3 |
PageRank | References | Authors |
0.38 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yenisel Plasencia Calana | 1 | 44 | 5.41 |
| Mauricio Orozco-Alzate | 2 | 79 | 17.27 |
| Heydi Mendez Vazquez | 3 | 91 | 7.10 |
| Edel Garcia-Reyes | 4 | 95 | 12.84 |
| Robert P. W. Duin | 5 | 4322 | 336.00 |