A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings
The Internet of Things (IoT) has transformed modern living and infrastructure by driving the development of sustainable smart buildings and enhancing building rehabilitation processes. In smart buildings, efficient machine learning (ML) classification of IoT devices is critical for improving securit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Springer
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/42940/ |
| Summary: | The Internet of Things (IoT) has transformed modern living and infrastructure by driving the development of sustainable smart buildings and enhancing building rehabilitation processes. In smart buildings, efficient machine learning (ML) classification of IoT devices is critical for improving security, optimizing resource management, and maintaining occupant comfort. Feature selection techniques are vital for boosting the effectiveness of machine learning models when categorizing Internet of Things (IoT) device classification for various reasons. Hence, this study provides an in-depth understanding of integrating IoT, ML, and smart buildings for device classification. The reasons for classification may range from monitoring security, power consumption, resource allocation, maintenance, and rehabilitation scenarios in smart buildings. This study emphasizes the importance of feature selection models in enhancing the accuracy of classification and interpretability for diagnosing and managing smart building systems effectively. This study thoroughly provides the state of the art for feature selection techniques in detail with purpose. It evaluates the principles and the types of feature selection methods, including their applications. It also highlights the key issues and challenges faced in applying these techniques in smart building infrastructures. This study discusses the process of optimization of feature selection methods, which helps to improve the model’s effectiveness and speed up machine learning accuracy for secure smart building resilient structures for its monitoring and security. Lastly, we provide a detailed discussion and suggestions along with future perspectives. |
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