Name
Abstract | ||
|---|---|---|
Performance of classifier ensembles depends on the precision and on the diversity of the members of the ensemble. In this paper we present an experimental study in which the relationship between the accuracy of the ensemble and both the diversity and the accuracy of base learners is analyzed. We conduct experiments on 8 different ANN ensembles and on 5 multiclass data sets. Experimental results show that a high diversity degree among the base learners does not always imply a high accuracy in the ensemble. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00374-6_11 | ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2018 |
Keywords | DocType | Volume |
Ensemble of classifiers,Diversity,Accuracy,ANN | Conference | 11160 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Paz Sesmero Lorente | 1 | 6 | 2.78 |
| Juan Manuel Alonso-Weber | 2 | 5 | 1.75 |
| Alessandro Giuliani | 3 | 170 | 25.21 |
| Giuliano Armano | 4 | 325 | 42.89 |
| Araceli Sanchis | 5 | 357 | 40.26 |