Name
Abstract | ||
|---|---|---|
The concept of Diversity is now recognized as a key characteristic of successful ensembles of predictors. In this paper we investigate an algorithm to generate diversity locally in regression ensembles of neural networks, which is based on the idea of imposing a neighborhood relation over the set of learners. In this algorithm each predictor iteratively improves its state considering only information about the performance of the neighbors to generate a sort of local negative correlation. We will assess our technique on two real data sets and compare this with Negative Correlation Learning, an effective technique to get diverse ensembles. We will demonstrate that the local approach exhibits better or comparable results than this global one. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840817_28 | ICANN (1) |
Keywords | Field | DocType |
negative correlation learning,local approach,neural network,key characteristic,effective technique,diverse ensemble,comparable result,local negative correlation,neighborhood relation,local diversity,predictor iteratively,ensemble learning | Negative correlation,Data set,Regression,Regression analysis,Computer science,sort,Correlation,Artificial intelligence,Artificial neural network,Ensemble learning,Machine learning | Conference |
Volume | ISSN | ISBN |
4131 | 0302-9743 | 3-540-38625-4 |
Citations | PageRank | References |
4 | 0.44 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Ñanculef | 1 | 53 | 10.64 |
| Carlos Valle | 2 | 21 | 8.20 |
| Héctor Allende | 3 | 148 | 31.69 |
| Claudio Moraga | 4 | 612 | 100.27 |