Imbalanced big data classification based on virtual reality in cloud computing

Xie, Wen-da and Cheng, Xiaochun ORCID logoORCID: (2020) Imbalanced big data classification based on virtual reality in cloud computing. Multimedia Tools and Applications, 79 (23-24) . pp. 16403-16420. ISSN 1380-7501 [Article] (doi:10.1007/s11042-019-7317-x)


Currently, there are many problems in imbalanced big data classification based on rough set with virtual reality technology in cloud computing. For example, redundant big data cleaning is not clear, the effect is poor for big data denoising and feature extraction, and the precision of classification is low. In this paper, an imbalanced big data classification is proposed based on Hubness and K nearest neighbor to address such problems. First, the SNM algorithm is used in order to efficient cleaning of redundant big data. Then, wavelet threshold denoising algorithm is used to denoise the big data to improve the denoising effect. Meantime, feature of big data is extracted based on Lyapunov theorem. Moreover, the Hubness and K-nearest neighbor algorithms are used to achieve high precision of imbalanced big data classification. Experiments verify that the proposed method effectively strengthens current cleaning and denoising methods of redundant imbalanced big data, as well as improves accuracy of extraction and classification of big data

Item Type: Article
Keywords (uncontrolled): Virtual reality, Cloud computing, Big data, Imbalance, Classification
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 26162
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Depositing User: Xiaochun Cheng
Date Deposited: 11 Feb 2019 09:37
Last Modified: 18 Aug 2021 12:33

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