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In developing a high-performance generic algorithm capable of managing multiple environments, the main challenges are the large and very different data sets and the problem of different types of residential spaces. The human activity recognition domain in residential environments has registered an exponential growth lately. In developing a high-performance generic algorithm capable of managing multiple environments, the main challenges are the large and very different data sets and the problem of different types of residential spaces. The training process of supervised algorithms is also limited since most of the available datasets do not have the activities data labeled. This article describes a classification method for dataset data. The used dataset is Kyoto generated by CASAS. The raw data is cleaned in the pre-processing phase, and the new features are extracted. These extracted features form a new …