Hierarchical Multi Twin Support Vector Machine
Vol. 130 No. 2B (2021), Research Articles
Vol. 130 No. 2B (2021)

Hierarchical Multi Twin Support Vector Machine

Research Articles
https://doi.org/10.26459/hueunijtt.v130i2B.5829
Published 2021-07-07
Nguyen The Cuong
Department of Mathematics, Communication University
Forth Coming Paper

Keywords

Support vector machines
Twin support vector machines

Abstract

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.
https://doi.org/10.26459/hueunijtt.v130i2B.5829
Forth Coming Paper

References

  1. V.N. Vapnik (1995), The nature of statistical learning theory. Springer, New York.
  2. W.S. Noble (2004), Support vector machine applications in computational Biology. MIT Press.
  3. M.M. Adankon, M. Cheriet (2009), Model selection for the LS-SVM. Application to handwriting recognition, Pattern Recognition 42, 3264-3270.
  4. Y. Tian, Y. Shi, X. Liu (2012), Recent advances on support vector machines research, Technological and Economic Development of Economy 18, 5-33.
  5. Jayadeva, R. Khemchandani and S. Chandra (2007), Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 5.
  6. D. Yeung, D. Wang, W. Ng. E. Tsang, X. Wang (2007), Structured large margin machines: sensitive to data distribution, Machine Learning 68, 171-200.
  7. H. Xue, S. Chen, Q. Yang (2011), Structural regularized support vector machine: a framework for structural large margin classifier, IEEE Transactions on Neural Networks 22, 573-587, http://dx.doi.org/10.119/TNN.2011.2108315.
  8. Z. Qi, Y. Tian, Y. Shi (2013), Structural twin support vector machine for classification, Knowledge-Base Systems 43, 74-81.
  9. http://archive.ics.uci.edu/ml/machine-learning-databases/
  10. B. Scholkopf and A. Smola (2002), Learning with kernel. Cambridge, Mass.: MIT Press.
  11. G. Fung and O. L Mangasarian (2001), “Proximal support vector machine,” Proc. Seventh Int'l Conf. Knowledge Discovery and Data mining, pp. 77-86.
  12. O. L. Mangasarian and E. W. Wild (2006), “Multisurface proximal support vector classification via generalized eigenvalues,” IEEE Trans. Pattern analysis and machine learning, vol. 28, no. 1, pp. 69-74.
  13. https://github.com/makeho8/python.
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2021 Array