Abstract
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.
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Acknowledgments
This work is supported by Natural Science Foundation of Inner Mongolia [No. 2018MS6010]; Foundation Science Research Start-up Fund of Inner Mongolia Agriculture University. [JC2016005]; Scientific Research Foundation for Doctors of Inner Mongolia Agriculture University. [NDYB2016-11].
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Xie, Wd., Cheng, X. Imbalanced big data classification based on virtual reality in cloud computing. Multimed Tools Appl 79, 16403–16420 (2020). https://doi.org/10.1007/s11042-019-7317-x
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DOI: https://doi.org/10.1007/s11042-019-7317-x