Name
Abstract | ||
|---|---|---|
In this work, an energy-based clustering method is used to prune heterogeneous ensembles. Specifically, the classifiers are grouped according to their predictions in a set of validation instances that are independent from the ones used to build the ensemble. In the empirical evaluation carried out, the cluster that minimizes the error in the validations set, besides reducing computational costs for storage and the prediction times, is almost as accurate as the complete ensemble. Furthermore, it outperforms subensembles that summarize the complete ensemble by including representatives from each of the identified clusters. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01418-6_34 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT I |
Keywords | Field | DocType |
Machine learning, Clustering analysis, Classifier ensembles, Bagging, Random forests | Cluster (physics),Computer science,Artificial intelligence,Cluster analysis,Random forest,Machine learning,Pruning | Conference |
Volume | ISSN | Citations |
11139 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javier Cela | 1 | 0 | 0.34 |
| Alberto Suárez | 2 | 487 | 22.33 |