Abstract
In order to improve the accuracy of the classification of the big data of disease gene detection, an algorithm for the classification of the big data of disease gene detection based on the complex network technology was proposed. On the basis of complex network technology, a distance-based membership function is first established. Considering the distance between the sample and the class center, the membership function of sample compactness is designed to complete the establishment of membership function of complex network. Combined with the design of the classification algorithm flow of the big data of disease gene detection, the design of the data classification algorithm was completed, and the classification of the big data of disease gene detection was realized. The experimental results show that the proposed algorithm is more accurate than the other two classification algorithms in the big data sets of different disease genes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lu, H., Yang, L., Yan, K., et al.: A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing 228, 270–276 (2017)
Suarez-Cetrulo, A.L., Cervantes, A.: An online classification algorithm for large scale data streams: iGNGSVM. Neurocomputing 262, 67–76 (2017)
Xing, W., Bei, Y.: Medical health big data classification based on KNN classification algorithm. IEEE Access PP(99), 1 (2019)
Ahsen, M.E., Ayvaci, M.U.S., Raghunathan, S.: When algorithmic predictions use human-generated data: a bias-aware classification algorithm for breast cancer diagnosis. Inf. Syst. Res. 30(1), 97–116 (2019)
Pourpanan, F., Lim, C.P., Wang, X., et al.: A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification. Neurocomputing 333, 440–451 (2019)
Shuai, L., Mengye, L., Hanshuang, L., Yongchun, Z.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
Jiang, C., Li, Y.: Health big data classification using improved radial basis function neural network and nearest neighbor propagation algorithm. IEEE Access 99, 1 (2019)
Jian, S., Yunlong, D., Bo, L., et al.: Optimization algorithm of redundant data classification in distributed database scenarios. Boletin Tecnico/Tech. Bull. 55(16), 54–61 (2017)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
Padillo, F., Luna, J.M., Ventura, S.: A grammar-guided genetic programing algorithm for associative classification in big data. Cogn. Comput. 11(3), 331–346 (2019)
Liu, S., Yang, G. (eds.): ADHIP 2018. LNICST, vol. 279, pp. 1–594. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19086-6
Owsiski, J.W., Kacprzyk, J., Shyrai, S., et al.: A heuristic algorithm of possibilistic clustering with partial supervision for classification of the intuitionistic fuzzy Data. J. Multiple-Valued Logic Soft Comput. 31(4), 399–423 (2018)
Liu, S., Liu, D., Srivastava, G., et al.: Overview and methods of correlation filter algorithms in object tracking. Complex Intell. Syst. (2020). https://doi.org/10.1007/s40747-020-00161-4
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Gao, Yy., Xiang, J., Tang, Yn., He, M., Li, W. (2021). Research on Big Data Classification Algorithm of Disease Gene Detection Based on Complex Network Technology. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-030-67871-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-67871-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67870-8
Online ISBN: 978-3-030-67871-5
eBook Packages: Computer ScienceComputer Science (R0)