Weighted vote for trees aggregation in Random Forest

Autor:	Nesma Settouti, Mohammed Amine Chikh, Mohammed El Amine Lazouni, Mostafa El Habib Daho
Rok vydání:	2014
Předmět:	Majority rule Computer science business.industry Ensemble prediction Statistics Weighted voting Pattern recognition Deviance (statistics) Artificial intelligence business Random forest
Zdroj:	ICMCS
DOI:	10.1109/icmcs.2014.6911187
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::de4ead8fe0533e8c1bea93bf35aa7530 https://doi.org/10.1109/icmcs.2014.6911187 Zobrazit plný text záznamu