Name
Abstract | ||
|---|---|---|
Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers U these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier's importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier's accuracy and their diversity. The sub-optimal selected subset presents good results against selection methods from the literature, being superior in 67.22% of the cases. The second approach, the integration, is named Centrality Based Fusion (CBF). This approach is a weighted combination method, which is superior to literature in 70% of the cases. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1142/S0218213020600040 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | DocType | Volume |
Multiple classifier systems, fusion, selection, diversity, centrality measures | Journal | 29 |
Issue | ISSN | Citations |
3-4 | 0218-2130 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronan Assumpção Silva | 1 | 0 | 0.34 |
| Alceu Britto | 2 | 94 | 18.30 |
| Fabrício Enembreck | 3 | 274 | 38.42 |
| Robert Sabourin | 4 | 908 | 61.89 |
| Luiz S. Oliveira | 5 | 476 | 47.22 |