Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things
Abstract Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog co...
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | IET Information Security |
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| Online Access: | https://doi.org/10.1049/ise2.12091 |
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| author | Junyu Ren Haibin Wan Chaoyang Zhu Tuanfa Qin |
| author_facet | Junyu Ren Haibin Wan Chaoyang Zhu Tuanfa Qin |
| author_sort | Junyu Ren |
| collection | DOAJ |
| description | Abstract Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods. |
| format | Article |
| id | doaj-art-b96ec5ddf3294e9b80f14fadc0186ad1 |
| institution | Kabale University |
| issn | 1751-8709 1751-8717 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Information Security |
| spelling | doaj-art-b96ec5ddf3294e9b80f14fadc0186ad12025-02-03T01:29:43ZengWileyIET Information Security1751-87091751-87172023-03-0117226928810.1049/ise2.12091Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical thingsJunyu Ren0Haibin Wan1Chaoyang Zhu2Tuanfa Qin3School of Electronic and Information Engineering South China University of Technology Guangzhou ChinaSchool of Computer and Electronic Information Guangxi University Nanning ChinaSchool of Electronic and Information Engineering South China University of Technology Guangzhou ChinaSchool of Computer and Electronic Information Guangxi University Nanning ChinaAbstract Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods.https://doi.org/10.1049/ise2.12091ensemble learningfeature selectionIoMTtrustworthiness prediction |
| spellingShingle | Junyu Ren Haibin Wan Chaoyang Zhu Tuanfa Qin Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things IET Information Security ensemble learning feature selection IoMT trustworthiness prediction |
| title | Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things |
| title_full | Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things |
| title_fullStr | Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things |
| title_full_unstemmed | Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things |
| title_short | Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things |
| title_sort | stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things |
| topic | ensemble learning feature selection IoMT trustworthiness prediction |
| url | https://doi.org/10.1049/ise2.12091 |
| work_keys_str_mv | AT junyuren stackingensemblelearningwithheterogeneousmodelsandselectedfeaturesubsetforpredictionofservicetrustininternetofmedicalthings AT haibinwan stackingensemblelearningwithheterogeneousmodelsandselectedfeaturesubsetforpredictionofservicetrustininternetofmedicalthings AT chaoyangzhu stackingensemblelearningwithheterogeneousmodelsandselectedfeaturesubsetforpredictionofservicetrustininternetofmedicalthings AT tuanfaqin stackingensemblelearningwithheterogeneousmodelsandselectedfeaturesubsetforpredictionofservicetrustininternetofmedicalthings |