MFES Framework for Efficient Feature Selection Among Subsystems in Intelligent Building

Conference paper


Babakura, A., Roko, A., Bui, A., Saidu, I. and Yusuf, M. A. 2022. MFES Framework for Efficient Feature Selection Among Subsystems in Intelligent Building. ICCBI 2021: International Conference on Computer Networks, Big Data and IoT. Online 08 - 09 Dec 2021 Springer, Cham. https://doi.org/10.1007/978-981-19-0898-9_33
AuthorsBabakura, A., Roko, A., Bui, A., Saidu, I. and Yusuf, M. A.
TypeConference paper
Abstract

The increasing trend of problem representation and high-dimensional data collection calls for the utilization of feature selection in many machines learning tasks and big data representations. However, identifying meaningful features from thousands of related features in the smart home data which are dissimilar in nature remains a nontrivial task. This has prompted for the deployment of a feature selection algorithm (FSA) that provides two possible solutions. First, to provide an efficient scheme that best optimizes the features for subsystem decisions and second, tackles feature subset selection bias problem. In this paper, a MFES framework for feature selection is proposed that uses a hybrid mechanism to tackle the problem of feature subset selection bias in intelligent building data. The mechanism uses the effectiveness of filters and accuracy of wrappers to obtain significant features for prediction. The proposed MFES framework resulted in 92.17% of accuracy as compared to the baseline approach resulting in 87.21% of accuracy. The experimental results show that efficient and better prediction accuracy can be achieved with a smaller feature set.

Keywordsfeature selection; machine learning; simulated annealing; algorithm (SAA); F-score; info-gain
Year2022
ConferenceICCBI 2021: International Conference on Computer Networks, Big Data and IoT
PublisherSpringer, Cham
Accepted author manuscript
License
File Access Level
Anyone
Publication dates
Online21 May 2022
Print22 May 2022
Publication process dates
Deposited05 Dec 2023
Journal citationpp. 421-435
ISSN2367-4520
Book titleComputer Networks, Big Data and IoT: Proceedings of ICCBI 2021
Book editorPandian, A. P.
Fernando, X.
Haoxiang, W.
ISBN9789811908989
9789811908972
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-19-0898-9_33
Web address (URL) of conference proceedingshttps://link.springer.com/book/10.1007/978-981-19-0898-9
Copyright holder© 2022, The Authors
Copyright informationUse of archived accepted manuscripts (AMs) of non open-access books and chapters are subject to an embargo period and Springer Nature's terms of use, which permit users to view, print, copy, download and text and data-mine the content, for the purposes of academic research, subject always to the full conditions of use. Under no circumstances may the AM be shared or distributed under a Creative Commons, or other form of open access license, nor may it be reformatted or enhanced.
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Accepted author manuscript
Mahmud Ahmed MFES Framework.pdf
License: Springer Nature terms of use for archived author accepted manuscripts (AAMs) of subscription articles, books and chapters
File access level: Anyone

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