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- W4206121118 abstract "AbstractThe classification of the daily living activities (DLAs) is a challenging research topic as the analysis of the DLAs data can help to improve the general health condition of the monitored subjects, to identify patterns that indicate various types of diseases such as the Alzheimer’s disease and to prevent the falls caused by the poor health condition. The DLAs classification is approached in this article using a machine learning methodology as follows: (1) the standard Cat Swarm Optimization (CSO) algorithm is adapted to a binary version (BCSO) applicable for features selection, (2) the features are selected using the BCSO algorithm, (3) the cats are evaluated considering the sum of square errors of the DLAs and (4) the DLAs data is classified by an ensemble based on six machine learning classification algorithms. The data used in experiments is the PAMAP2 Physical Activity Monitoring dataset from the UCI Machine Learning Repository.KeywordsDaily living activitiesClassificationFeatures selectionBinary cat swarm optimizationGradient boosted trees" @default.
- W4206121118 created "2022-01-26" @default.
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- W4206121118 date "2022-01-01" @default.
- W4206121118 modified "2023-09-26" @default.
- W4206121118 title "Binary Cat Swarm Optimization Feature Selection and Machine Learning Based Ensemble for the Classification of the Daily Living Activities of the Elders" @default.
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- W4206121118 doi "https://doi.org/10.1007/978-3-030-93564-1_21" @default.
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