Imbalanced big data classification based on virtual reality in cloud computing

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.

[1]  Arun Kumar Sangaiah,et al.  Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces , 2018, IEEE Access.

[2]  Kenli Li,et al.  A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[3]  张保明 Zhang Bao-ming,et al.  Classification of airborne LiDAR point cloud data based on information vector machine , 2016 .

[4]  Jianda Han,et al.  Missing-Data Classification With the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition , 2015, IEEE Transactions on Industrial Electronics.

[5]  Zhiming Chen,et al.  Detection and classification from electromagnetic induction data , 2013, J. Comput. Phys..

[6]  Arun Kumar Sangaiah,et al.  Visual attention feature (VAF) : A novel strategy for visual tracking based on cloud platform in intelligent surveillance systems , 2018, J. Parallel Distributed Comput..

[7]  Qi Zhang,et al.  Deep learning-based tree classification using mobile LiDAR data , 2015 .

[8]  U. Panne,et al.  Gas chromatography‐mass spectral analysis of roots of Echinacea species and classification by multivariate data analysis , 1998 .

[9]  Jonas Bohlin,et al.  Combining point clouds from image matching with SPOT 5 multispectral data for mountain vegetation classification , 2015 .

[10]  Yu-Chi Lee,et al.  Taiwanese adult foot shape classification using 3D scanning data , 2015, Ergonomics.

[11]  Yi-Hung Huang,et al.  Feature selection based on an improved cat swarm optimization algorithm for big data classification , 2016, The Journal of Supercomputing.

[12]  Shuai Liu,et al.  A Novel Distance Metric: Generalized Relative Entropy , 2017, Entropy.

[13]  Hari M. Srivastava,et al.  Parallel Fractal Compression Method for Big Video Data , 2018, Complex..

[14]  Giovanni Felici,et al.  CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules , 2015, Bioinform..

[15]  D. Ellison.  Multiple Molecular Data Sets and the Classification of Adult Diffuse Gliomas. , 2015, The New England journal of medicine.

[16]  Zhiping Lin,et al.  Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification , 2016, Neural Networks.

[17]  P. Simmonds,et al.  Methods for virus classification and the challenge of incorporating metagenomic sequence data. , 2015, The Journal of general virology.

[18]  Jiantao Zhou,et al.  Distribution of primary additional errors in fractal encoding method , 2014, Multimedia Tools and Applications.

[19]  Ming Ma,et al.  A fractal image encoding method based on statistical loss used in agricultural image compression , 2015, Multimedia Tools and Applications.

[20]  Linpeng Huang,et al.  HMVFS: A Versioning File System on DRAM/NVM Hybrid Memory , 2018, J. Parallel Distributed Comput..

[21]  Lin Zhu,et al.  A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification , 2016, Inf. Sci..