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Cluster Computing

Erschienen in:

20.02.2018

Decision making based on grey model and support vector machine

verfasst von: Li Futou, Liu Liang

Erschienen in: Cluster Computing | Sonderheft 2/2019

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Abstract

In order to improve the effectiveness of market preference early warning analysis algorithm, a new method based on gray kernel AR- SVM model is proposed. Firstly, we used support vector machine (SVM) algorithm to construct the financial market risk warning analysis model, which includes no extreme risk and extreme risk in two cases, and used SVM algorithm to find the optimal classification process based on the training set; Secondly, the SVM model is prone to extreme risk warning “failure” in the market preference prediction problem in the market preference data records are processed by the improved gray model, and the mixed kernel function was used to improve the SVM algorithm, which realized the sample data to improve the prediction performance of autoregressive model. Finally, the SVM algorithm is used to improve the accuracy of the market model. The experimental results show that the proposed method is effective in the analysis of market preference data.

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Anand, S., Chandramouli, R.M.: A micro-finance model for spectrum management in wireless networks. IEEE Trans. Cogn. Commun. Netw. 2(3), 263–272 (2016)

Varma, J.R., Virmani, V.: Computational finance using QuantLib-Python. Comput. Sci. Eng. 18(2), 78–88 (2016)

Burr, M.T., Zimmer, M.J., Warner, G., et al.: Emerging models for microgrid finance: driven by the need to deliver value to end users. IEEE Electrification Mag. 2(1), 30–39 (2014)

Shiao, H.-T., Cherkassky, V., Lee, J., et al.: SVM-based system for prediction of epileptic seizures from iEEG signal. IEEE Trans. Biomed. Eng. 64(5), 1011–1022 (2017)

Singh, D., Roy, D., Mohan, C.K.: DiP-SVM: distribution preserving kernel support vector machine for big data. IEEE Trans. Big Data 3(1), 79–90 (2017)

Sukawattanavijit, C., Chen, J., Zhang, H.: GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(3), 284–288 (2017)

Reis, P.M.G.I., da Costa, J.P.C.L., Miranda, R.K., et al.: ESPRIT-Hilbert-based audio tampering detection with SVM classifier for forensic analysis via electrical network frequency. IEEE Trans. Inf. Forensics Secur. 12(4), 853–864 (2017)

Park, S., Lee, H.S., Kim, J.: Seed growing for interactive image segmentation using SVM classification with geodesic distance. Electron. Lett. 53(1), 22–24 (2017)

Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recognit. Lett. 94, 112–117 (2017)

10.

Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)

11.

Arunkumar, N., Ram Kumar, K., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, tsallis entropy and kolmogorov complexity. J. Med. Imaging Health Inform. 6(2), 526–531 (2016)

12.

Wu, Y., Yang, X., Plaza, A., et al.: Approximate computing of remotely sensed data: SVM hyperspectral image classification as a case study. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 9(12), 5806–5818 (2016)

13.

Sriwastava, B.K., Basu, S., Maulik, U.: Predicting protein-protein interaction sites with a novel membership based fuzzy SVM classifier. IEEE/ACM Trans. Comput. Biol. Bioinf. 12(6), 1394–1404 (2015)

14.

Ai, Y., Shao, X., Jiang, P., et al.: Erratum to: process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials. Appl. Phys. A 121(3), 1317–1318 (2015)

15.

Xu, S.J., Han, J.Q., Liu, G.H., et al.: The single best artificial neural network and neural network ensemble in modeling of palladium microextraction. Monatshefte für Chemie (Chem Mon.) 146(8), 1217–1227 (2015)

16.

Zhang, M., Tang, J., Zhang, X., et al.: Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples. Chin. J. Mech. Eng. 29(2), 396–405 (2016)MathSciNet

17.

Shamshirb, S., Mohammadi, K., Tong, C.W., et al.: Ahybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor. Appl. Climatol. 125(1), 53–65 (2016)

Titel: Decision making based on grey model and support vector machine
verfasst von: Li Futou
Liu Liang
Publikationsdatum: 20.02.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2227-7

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