Name
Abstract | ||
|---|---|---|
Random forest models [1] consist of an ensemble of randomized decision trees. It is one of the best performing classification models. With this idea in mind, in this section we introduced a random split operator based on a Bayesian approach for building a random forest. The convenience of this split method for constructing ensembles of classification trees is justified with an error bias-variance decomposition analysis. This new split operator does not clearly depend on a parameter K as its random forest's counterpart, and performs better with a lower number of trees. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02906-6_41 | ECSQARU |
Keywords | Field | DocType |
bayesian random split,lower number,random forest model,classification trees,bayesian approach,classification model,random split operator,build ensembles,new split operator,classification tree,split method,error bias-variance decomposition analysis,random forest,decision tree,variance decomposition | Decision tree,Random graph,Computer science,Operator (computer programming),Artificial intelligence,Random forest,Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
5590 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Cano | 1 | 193 | 20.06 |
| Andrés R. Masegosa | 2 | 256 | 26.13 |
| Serafín Moral | 3 | 1218 | 145.79 |