Name
Title | ||
|---|---|---|
The implication of data diversity for a classifier-free ensemble selection in random subspaces |
Abstract | ||
|---|---|---|
Abstract Ensemble,of Classifiers (EoC) has,been,shown effective in improving,the performance,of single classifiers by,combining,their outputs. By using diverse data subsets to train classifiers, the ensemble creation methods can create diverse classifiers for the EoC. In this work, we propose a scheme to measure the data diversity directly from random,subspaces and we explore the possibility of using the data diversity directly to select the best data,subsets for the construction of the EoC. The applicability is tested on NIST SD19 handwritten numerals. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761767 | Tampa, FL |
Keywords | Field | DocType |
data handling,pattern classification,classifier training,classifier-free ensemble selection,data diversity,ensemble creation methods,random subspaces | Pattern recognition,Computer science,Random subspace method,Linear subspace,Feature extraction,Data diversity,NIST,Artificial intelligence,Classifier (linguistics),Statistical classification,Group method of data handling,Machine learning | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-2174-9 | 4 |
PageRank | References | Authors |
0.44 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert Hung-Ren Ko | 1 | 229 | 11.85 |
| Robert Sabourin | 2 | 908 | 61.89 |
| L. S. Oliveira | 3 | 385 | 25.17 |
| Alceu Britto | 4 | 94 | 18.30 |