Name
Abstract | ||
|---|---|---|
Random Forest RF is a successful technique of ensemble prediction that uses the majority voting or an average depending on the combination. However, it is clear that each tree in a random forest can have different contribution to the treatment of some instance. In this paper, we show that the prediction performance of RF's can still be improved by replacing the GINI index with another index (twoing or deviance). Our experiments also indicate that weighted voting gives better results compared to the majority vote. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMCS.2014.6911187 | Multimedia Computing and Systems |
Keywords | DocType | ISSN |
decision trees,neural nets,ensemble prediction,random forest,trees aggregation,weighted vote,classification,decision tree,vegetation,radio frequency,sensitivity,indexes | Conference | 2472-7652 |
Citations | PageRank | References |
2 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| El Habib Daho, M. | 1 | 2 | 0.71 |
| Nesma Settouti | 2 | 37 | 6.33 |
| El Amine Lazouni, M. | 3 | 2 | 0.37 |
| El Amine Chikh, M. | 4 | 2 | 0.37 |