Volume 44 Issue 1
May.  2024
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REFINED HUMAN ACTIVITY CLASSIFICATION VIA MACHINE LEARNING TECHNIQUES: A METHODOLOGICAL EXPLORATION WITHIN THE FUZZY ROUGH DOMAIN

  • This study focuses on enhancing human activity classification using advanced machine learning techniques within a unique fuzzy rough framework. The framework, designed to handle imprecise and uncertain data, significantly boosts the classification accuracy of traditional ML algorithms. By integrating fuzzy rough set theory, the study addresses the challenges of noisy datasets and complex activity patterns. The methodological exploration involves collecting accelerometer and gyroscope data from wearable devices, followed by preprocess­ ing for noise reduction and feature extraction. The innovative approach combines decision trees, support vector machines, and neural networks with fuzzy rough logic. Results indicate that the fuzzy rough-ML hybrids outperform conventional classifiers, especially in ambiguous conditions. This work underscores the framework's potential for practical applications and theoretical advancements, offering a robust foundation for future research in complex human activity classification scenarios.

     

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    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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