Abstract:The ensemble methods play a vital role in machine learning for obtaining a high-performing model for the study dataset, and combining multiple classifiers to build a best-predictive model. On the other hand, Feature selection helps to remove irrelevant variables in the dataset in order to construct better predictive models. Therefore this research aimed to develop a robust model for activity recognition for multi-residents in smart homes using the ARAS dataset. The study employed Tree-based feature selection t… Show more
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