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: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Springer
2025
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/42940/ |
| _version_ | 1848827309145980928 |
|---|---|
| author | Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Naqeeb Khan, Sundas Busaeed, Raed Abdullah Abobakr Fairooz, Towfeeq |
| author_facet | Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Naqeeb Khan, Sundas Busaeed, Raed Abdullah Abobakr Fairooz, Towfeeq |
| author_sort | Waseem, Quadri |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:58:40Z |
| format | Article |
| id | ump-42940 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:58:40Z |
| publishDate | 2025 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-429402025-09-03T08:22:36Z https://umpir.ump.edu.my/id/eprint/42940/ A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Naqeeb Khan, Sundas Busaeed, Raed Abdullah Abobakr Fairooz, Towfeeq QA75 Electronic computers. Computer science 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. Springer 2025-08-20 Article PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/42940/1/A%20Survey%20and%20Analysis%20of%20Feature%20Selection%20Techniques%20in%20Machine%20Learning.pdf pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/42940/7/A%20survey%20and%20analysis%20of%20feature%20selection%20techniques%20in%20machine%20learning%20for%20IoT%20device%20classification%20within%20smart%20buildings.pdf Waseem, Quadri and Wan Isni Sofiah, Wan Din and Azamuddin, Ab Rahman and Naqeeb Khan, Sundas and Busaeed, Raed Abdullah Abobakr and Fairooz, Towfeeq (2025) A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings. Innovative Infrastructure Solutions, 10 (9). pp. 1-16. ISSN 2364-4176. (Published) https://doi.org/10.1007/s41062-025-02203-7 https://doi.org/10.1007/s41062-025-02203-7 https://doi.org/10.1007/s41062-025-02203-7 |
| spellingShingle | QA75 Electronic computers. Computer science Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Naqeeb Khan, Sundas Busaeed, Raed Abdullah Abobakr Fairooz, Towfeeq A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings |
| title | A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings |
| title_full | A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings |
| title_fullStr | A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings |
| title_full_unstemmed | A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings |
| title_short | A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings |
| title_sort | survey and analysis of feature selection techniques in machine learning for iot device classification within smart buildings |
| topic | QA75 Electronic computers. Computer science |
| url | https://umpir.ump.edu.my/id/eprint/42940/ https://umpir.ump.edu.my/id/eprint/42940/ https://umpir.ump.edu.my/id/eprint/42940/ |