[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123429-en":3,"doc-seo-123429-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123429,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings - Review","The Internet of Things (IoT) enables sustainable smart buildings and accelerates digital transformation, making accurate machine learning (ML) classification of IoT devices essential for cyber defense, resource management, and occupant comfort. Feature selection techniques strengthen classification performance and interpretability, supporting diagnosis and effective system management. The study examines the integration of IoT and ML in smart buildings, reviews state-of-the-art feature selection methods and their purposes, and discusses key issues, challenges, optimization processes, and future directions for secure, resilient infrastructure.","A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings  \nWaseem, Q. , Din, W. I. S. B. W. , Rahman, A. B. A. , Khan, S. N. , Busaeed, R. A. A. , & Fairooz, T. (2025) . A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings. Innovative Infrastructure Solutions, 10(9), 1-19 . Article 407. [https://doi.org/10.1007/s41062-025-](https://doi.org/10.1007/s41062-025-)[ ](https://doi.org/10.1007/s41062-025-)02203-7  \nLink to publication record in Ulster University Research Portal  \nPublished in:  \nInnovative Infrastructure Solutions  \nPublication Status:  \nPublished (in print/issue): 20/08/2025  \nDOI:  \n10.1007/s41062-025-02203-7  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument Licence:  \nCC BY  \nFor Author Accepted Manuscripts (AAM) published under Ulster University's Rights Retention Policy for Scholarly Works (RRPSW)  \nWhen citing an AAM published under Ulster University's RRPSW please use the following citation structure:  \nAuthor, A. A. (Year) . Title of article. Journal Name,[Accepted Author Manuscript] . PURE Portal URL. Licensed under CC BY 4.0.  \nGeneral rights  \nThe copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence.  \nUnless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) .  \nIf the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) .  \nTake down policy  \nThe Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk)  \nDownload date: 03/08/2026  \nInnovative Infrastructure Solutions (2025) 10:407 [https://doi.org/10.1007/s41062-025-02203-7](https://doi.org/10.1007/s41062-025-02203-7)  \nREVIEW  \nA survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings  \nQuadriWaseem1,2 · Wan Isni Sofiah BintiWan Din1 · Azamuddin Bin Ab Rahman1 · Sundas Naqeeb Khan3 · Raed Abdullah Abobakr Busaeed1 · Towfeeq Fairooz4  \nReceived: 30 November 2024 / Accepted: 23 July 2025 / Published online: 20 August 2025 © The Author(s) 2025  \nAbstract  \nThe Internet of Things (IoT) has revolutionized modern living and infrastructure by driving the development of sustainable smart buildings and accelerating the digital transformation of buildings. In smart buildings, efficient machine learning (ML) based classification of IoT devices is critical for improving cyber defense, optimizing resource management, and maintaining occupant comfort. Feature selection techniques are vital for boosting the effectiveness of machine learning models when classifying and categorizing Internet of Things (IoT) devices for various reasons. Hence, this study initially provides an in-depth understanding of integrating IoT and ML in smart buildings. We provide the reasons and importance of device classification in smart buildings, which may range from monitoring security, power consumption, resource allocation, maintenance, and rehabilitation scenarios. This study emphasizes the importance of feature selection (FS) models in enhancing the accuracy of classification and interpreta","cbCailbx9Th9Cesv","https://ap.wps.com/l/cbCailbx9Th9Cesv","pdf",1333489,1,20,"English","en",105,"# Abstract\n# Introduction\n## IoT and smart building context\n## Importance of device classification\n## Role of feature selection in ML","[{\"question\":\"Why is ML-based IoT device classification important in smart buildings?\",\"answer\":\"It improves cyber defense, optimizes resource management, and helps maintain occupant comfort through accurate and effective device categorization.\"},{\"question\":\"What role do feature selection techniques play in this study?\",\"answer\":\"Feature selection enhances classification accuracy and interpretability, aiding diagnosis and management of smart building systems.\"},{\"question\":\"What does the survey cover regarding feature selection methods?\",\"answer\":\"It reviews the principles and types of feature selection methods, their purposes and applications, and the key issues and challenges in applying them to smart building infrastructures.\"}]","A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings - 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