EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 309 (2020) MATEC Web Conf., 309 (2020) 05013 References
Open Access
Issue
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 05013
Number of page(s) 6
Section Modelling and Simulation
DOI https://doi.org/10.1051/matecconf/202030905013
Published online 04 March 2020
  1. Khedher L, Ram rez J, Go rriz JM, Brahim A, Segovia F. Alzheimer’s disease neuroimaging initiative. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images.Neurocomputing 2014. [Google Scholar]
  2. Xie J, Wang C. Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato- squamous diseases. Expert Syst Appl 2011;38(5):5809–15. [CrossRef] [Google Scholar]
  3. Wang TY, Chiang HM. One-against-one fuzzy support vector machine classifier: an approach to text categorization. Expert Syst Appl 2009;36(6):10030–4. [CrossRef] [Google Scholar]
  4. Wang TY, Chiang HM. Solving multi-label text categorization problem using support vector machine approach with member- ship function. Neurocomputing 2011;74(17):3682–9. [CrossRef] [Google Scholar]
  5. Li Y, Xia J, Zhang S, Yan J, Ai X, Dai K. An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst Appl 2012;39(1):424–30. [CrossRef] [Google Scholar]
  6. Jayadeva, Khemchandani R, Chandra S. Twin support vector machine for pattern classification. IEEE Trans Pattern Anal Mach Intell 2007;29(5):905–10. [CrossRef] [Google Scholar]
  7. Shao YH, Zhang CH, Wang XB, Deng NY. Improvements on twin support vector machines. IEEE Trans Neural Netw 2011;22(6). [Google Scholar]
  8. Peng X. A v-twin support vector machine classifier and its geometric algorithms. Inf Sci 2010;180:3863–75. [CrossRef] [Google Scholar]
  9. Kumar MA, Gopal M. Least squares twin support vector machines for pattern classification. Expert Syst Appl 2009;36:7535–43. [CrossRef] [Google Scholar]
  10. Xu Y, Guo R, Wang L. A twin multi-class classification support vector machine. Cogn Comput 2013;5(4):580–8. [CrossRef] [Google Scholar]
  11. Peng X. TSVR: an efficient twin support vector machine for regression. Neural Netw 2010;23:365–72. [CrossRef] [Google Scholar]
  12. Peng X, Xu D, Shen J. A twin projection support vector machine for data regression. Neurocomputing 2013;138:131–41. [CrossRef] [Google Scholar]
  13. N. V. Chawla, K. Bowyer, L. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of AI Research, vol. 16, pp. 321–357, 2002. [Google Scholar]
  14. S. Barua, M. M. Islam, X. Yao, and K. Murase, “MWMOTE— majority weighted minority oversampling technique for imbalanced data set learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 2, pp. 405–425, 2014. [CrossRef] [Google Scholar]
  15. Peng Cao, Dazhe Zhao, and Osmar Zaiane, An Optimized Cost-Sensitive SVM for Imbalanced Data Learning. PAKDD 2013, Part II, LNAI 7819, pp. 280–292, 2013. [Google Scholar]
  16. J. L. Hsu, P. C. Hung, and H. Y. Lin, “Applying under- sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer,” Journal of Medical Systems, vol. 39, no. 4, pp. 1–13, 2015. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.