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Random forests have been used as effective models to tackle a number of classification and regression problems. In this paper, we present a new type of Random Forests (RFs) called Red(uced)-RF that adopts a new voting mechanism called Priority Vote Weighting (PV) and a new dynamic data reduction principle which improve accuracy and execution time compared(More)
One goal of a social network, as its name suggests, is to provide human beings with a digital platform where they can build social relationships with a spectrum of people they choose. In this paper, we build a new model that uses Facebook data to measure inter-communication between segregated communities in Lebanon, a country whose diverse yet divided(More)
Motivation: With the growing significance of metatranscriptomic assemblies, the need to improve their quality and maintain their con-trollable size has become essential. That would help in boosting all applications based on metatranscriptomic assembly. In this paper, we propose a pipeline that filters de novo assemblies while preserving or improving their(More)
Random Forests have been used as effective ensemble models for classification. We present in this paper a new type of Random Forests (RFs) called Red(uced) RF that adopts a new dynamic data reduction principle and a new voting mechanism called Priority Vote Weighting (PV) which improve accuracy, execution time and AUC values compared to Breiman's RF. Red-RF(More)
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