Name
Abstract | ||
|---|---|---|
Ensemble learning has gained considerable attention in different tasks including regression, classification and clustering. Adaboost and Bagging are two popular approaches used to train these models. The former provides accurate estimations in regression settings but is computationally expensive because of its inherently sequential structure, while the latter is less accurate but highly efficient. One of the drawbacks of the ensemble algorithms is the high computational cost of the training stage. To address this issue, we propose a parallel implementation of the Resampling Local Negative Correlation (RLNC) algorithm for training a neural network ensemble in order to acquire a competitive accuracy like that of Adaboost and an efficiency comparable to that of Bagging. We test our approach on both synthetic and real datasets from the UCI and Statlib repositories for the regression task. In particular, our fine-grained parallel approach allows us to achieve a satisfactory balance between accuracy and parallel efficiency. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s11063-010-9157-6 | Neural Processing Letters |
Keywords | Field | DocType |
Ensemble learning,Artificial neural networks,Parallel algorithms,Local negative correlation,Resampling | AdaBoost,Pattern recognition,Regression,Parallel algorithm,Computer science,Supervised learning,Artificial intelligence,Artificial neural network,Cluster analysis,Ensemble learning,Resampling,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 3 | 1370-4621 |
Citations | PageRank | References |
6 | 0.41 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Valle | 1 | 21 | 8.20 |
| Francisco Saravia | 2 | 8 | 0.76 |
| Héctor Allende | 3 | 148 | 31.69 |
| Raúl Monge | 4 | 8 | 2.48 |
| C. Fernández | 5 | 35 | 8.76 |