Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74253
Title: Modeling latent relation to boost things categorization service
Authors: Chen, Y
Zhang, J
Xu, L
Guo, M
Cao, J 
Issue Date: 2017
Source: IEEE transactions on services computing, 2017, p. 2
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 patternMergeCell 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 
ISSN: 1939-1374
EISSN: 1939-1374
DOI: 10.1109/TSC.2017.2715159
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