Weighted vote for trees aggregation in Random Forest

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 predic...

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Bibliographic Details
Published in2014 International Conference on Multimedia Computing and Systems (ICMCS) pp. 438 - 443
Main Authors El Habib Daho, Mostafa, Settouti, Nesma, El Amine Lazouni, Mohammed, El Amine Chikh, Mohammed
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2014
Subjects
classification
decision tree
Decision trees
Indexes
Liver
Radio frequency
Random Forest
Sensitivity
Vegetation
Weighted vote
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DOI10.1109/ICMCS.2014.6911187

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Summary: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.
DOI:10.1109/ICMCS.2014.6911187