Name
Abstract | ||
|---|---|---|
The object selection is an important task for instance-based classifiers since through this process the size of a training set could be reduced and then the runtimes in both classification and training steps would be reduced. Several methods for object selection have been proposed but some methods discard relevant objects for the classification step. In this paper, we propose an object selection method which is based on the idea of sequential floating search. This method reconsiders the inclusion of relevant objects previously discarded. Some experimental results obtained by our method are shown and compared against some other object selection methods. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73499-4_52 | MLDM |
Keywords | Field | DocType |
important task,object selection,sequential floating search,object selection method,training step,training set,restricted sequential,relevant object,classification step,floating search applied,instance-based classifier | Training set,Pattern recognition,Method,Computer science,Artificial intelligence,Linear search,Machine learning | Conference |
Volume | ISSN | Citations |
4571 | 0302-9743 | 4 |
PageRank | References | Authors |
0.43 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Arturo Olvera-López | 1 | 120 | 5.61 |
| J. Francisco Martínez-Trinidad | 2 | 122 | 6.66 |
| J. Ariel Carrasco-Ochoa | 3 | 233 | 12.86 |