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http://hdl.handle.net/10397/105670
Title: | Modeling latent relation to boost things categorization service | Authors: | Chen, Y Zhang, J Xu, L Guo, M Cao, J |
Issue Date: | Sep-2020 | Source: | IEEE transactions on services computing, Sept-Oct. 2020, v. 13, no. 5, p. 915-929 | Abstract: | While it is well understood that the Internet of things (IoT) offers the capability of integrating the physical world and the cyber world, it also presents many significant challenges with numerous heterogeneous things connected and interacted, such as how to efficiently annotate things with semantic labels (i.e., things categorization) for searching and recommendation. Traditional ways for things categorization are not effective due to several characteristics (e.g., thing's text profiles are usually short and noise, things are heterogeneous in terms of functionality and attributes) of IoT. In this paper, we develop a novel things categorization technique to automatically predict semantic labels for a given thing. Our proposed approach formulates things categorization as a multi-label classification problem and learns a binary support vector machine classifier for each label to support multi-label classification. We extract two types of features to train classification model: 1) explicit feature from thing's profiles and spatial-temporal pattern; 2) implicit feature from thing's latent relation strength. We utilize a latent variable model to uncover thing's latent relation strength from their interaction behaviours. We conduct a comprehensive experimental study based on three real datasets, and the results show fusing thing's latent relation strength can significantly boost things categorization. | Keywords: | Interaction behaviours Internet of things Latent variable model Multi-label classification Things categorization |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on services computing | EISSN: | 1939-1374 | DOI: | 10.1109/TSC.2017.2715159 | Rights: | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication Y. Chen, J. Zhang, L. Xu, M. Guo and J. Cao, "Modeling Latent Relation to Boost Things Categorization Service," in IEEE Transactions on Services Computing, vol. 13, no. 5, pp. 915-929, 1 Sept.-Oct. 2020 is available at https://doi.org/10.1109/TSC.2017.2715159. |
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