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1篇 您的检索式：作者名="Amina Boublenza"

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		题名	作者	年代	出处	被引量
	1	A new correlation-based approach for ensemble selection in random forests显示文摘Purpose-Ensemble methods have been widely used in the field of pattern recognition due to the difficulty offinding a single classifier that performs well on a wide variety of problems.Despite the effectiveness of thesetechniques,studies have shown that ensemble methods generate a large number of hypotheses and thatcontain redundant classifiers in most cases.Several works proposed in the state of the art attempt to reduce allhypotheses without affecting performance.Design/methodology/approach-In this work,the authors are proposing a pruning method that takes intoconsideration the correlation between classifiers/classes and each classifier with the rest of the set.The authorshave used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by atechnique inspired by the CFS(correlation feature selection)algorithm.Findings-The proposed method CES(correlation-based Ensemble Selection)was evaluated onten datasets from the UCI machine learning repository,and the performances were compared to sixensemble pruning techniques.The results showed that our proposed pruning method selects a smallensemble in a smaller amount of time while improving classification rates compared to the state-of-the-artmethods.Originality/value-CES is a new ordering-based method that uses the CFS algorithm.CES selects,in a shorttime,a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-thearttechniques used in this study.	Mostafa El Habib Daho Nesma Settouti Mohammed El Amine Bechar Amina Boublenza Mohammed Amine Chikh	2021	International Journal of Intelligent Computing and Cybernetics2021，14，2：	1

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