Skip to main content
SpringerLink
Log in

Applications Based on TWSVM

  • Chapter
  • First Online:
Twin Support Vector Machines

Part of the book series: Studies in Computational Intelligence ((SCI,volume 659))

  • 1225 Accesses

Abstract

TWSVM and its variants have been widely discussed in earlier chapters. In this chapter, we tend to discuss the applications where the special properties of TWSVMs have been used. One of the major advantage of TWSVM is its superiority over other machine learning methodologies in dealing with unbalanced datasets. Such datasets naturally arise for e.g. in medical domain where samples of diseased patients is far less than normal patients resulting in unbalanced classes. The structure of TWSVM allows to identify the hyperplane close to samples of diseased patients. Thus via this chapter, we tend to make reader familiar with widespread applicability of TWSVM across a multitude of application domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced training sets: One-sided selection. (ICML), 97, 179–186 (1997).

    Google Scholar 

  2. Jo, T., & Japkowicz, N. (2004). Class imbalances versus small disjuncts. ACM SIGKDD Explorations Newsletter, 6(1), 40–49.

    Article  MathSciNet  Google Scholar 

  3. Barandela, R., Valdovinos, R. M., Sánchez, J. S., & Ferri, F. J. (2004). The imbalanced training sample problem: Under or over sampling. Structural, Syntactic, and Statistical Pattern Recognition, 806–814.

    Google Scholar 

  4. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 321–357.

    Google Scholar 

  5. Van H. J., Khoshgoftaar, T. M., & Napolitano, A. (2007). Experimental perspectives on learning from imbalanced data. In Proceedings of the 24th International Conference on Machine learning (pp. 935–942).

    Google Scholar 

  6. Estabrooks, A., Jo, T., & Japkowicz, N. (2004). A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1), 18–36.

    Article  MathSciNet  Google Scholar 

  7. Naik, R., & Kumar, D., & Jayadeva., (2010). Twin SVM for gesture classification using the surface electromyogram. IEEE Transactions on Information Technology in Biomedicine, 14(2), 301–308.

    Google Scholar 

  8. Kumar, D. K., Arjunan, S. P., & Singh, V. P. (2013). Towards identification of finger flexions using single channel surface electromyography-able bodied and amputee subjects. Journal of NeuroEngineering and Rehabilitation, 10(50), 1–7.

    Google Scholar 

  9. Arjunan, S. P., Kumar, D. K., & Naik, G. R. (2010). A machine learning based method for classification of fractal features of forearm sEMG using twin support vector machines, engineering in medicine and biology society (EMBC). In: 2010 Annual international conference of the IEEE (pp. 4821–4824).

    Google Scholar 

  10. Arjunan, S. P., Kumar, D. K., & Jayadeva. (2015). Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal, Biomedical Engineering/Biomedizinische Technik. doi:10.1515/bmt-2014-0134.

  11. Soman, S., & Jayadeva., (2015). High performance EEG signal classification using classifiability and the Twin SVM. Applied Soft Computing, 30, 305–318.

    Google Scholar 

  12. Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441–446.

    Article  Google Scholar 

  13. Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008). Filter bank common spatial pattern (FBCSP) in brain-computer interface. In IEEE International Joint Conference on Neural Networks, 2008. IJCNN, 2008 (pp. 2390–2397). IEEE World Congress on Computational Intelligence,

    Google Scholar 

  14. Novi, Q., Guan, C., Dat, T. H., & Xue, P. (2007). Sub-band common spatial pattern (SBCSP) for brain-computer interface. In 3rd International IEEE/EMBS Conference on Neural Engineering, 2007. CNE’07 (pp. 204–207).

    Google Scholar 

  15. Li, Y., Dong, M., & Kothari, R. (2005). Classifiability-based omnivariate decision trees. IEEE Transactions on Neural Networks, 16(6), 1547–1560.

    Article  Google Scholar 

  16. Zhang, X. (2009). Boosting twin support vector machine approach for MCs detection. In Asia-Pacific Conference on Information Processing, 2009. APCIP 2009 (vol. 1, pp. 149–152).

    Google Scholar 

  17. Zhang, X., Gao, X., & Wang, Y. (2009). MCs detection with combined image features and twin support vector machines. Journal of Computers, 4(3), 215–221.

    Article  MathSciNet  Google Scholar 

  18. Zhang, X., Gao, X., & Wang, Y. (2009). Twin support tensor machines for MCs detection. Journal of Electronics (China), 26(3), 318–325.

    Article  Google Scholar 

  19. Si, X., & Jing, L. (2009). Mass detection in digital mammograms using twin support vector machine-based CAD system. In WASE International Conference on Information Engineering, 2009. ICIE’09 (vol. 1, pp. 240–243). New York: IEEE.

    Google Scholar 

  20. Tomar, D., Prasad, B. R., Agarwal, S. (2004). An efficient parkinson disease diagnosis system based on least squares twin support vector machine and particle swarm optimization. In 2014 9th International IEEE Conference on Industrial and Information Systems (ICIIS) (pp. 1–6).

    Google Scholar 

  21. Tomar, D., & Agarwal, S. (2014). Feature selection based least square twin support vector machine for diagnosis of heart disease. International Journal of Bio-Science and Bio-Technology, 6(2), 69–82.

    Article  Google Scholar 

  22. Ding, X. Zhang, G., Ke, Y., Ma, B., & Li, Z. (2008). High efficient intrusion detection methodology with twin support vector machines. In International Symposium on Information Science and Engineering, 2008. ISISE’08 (vol. 1, pp. 560–564).

    Google Scholar 

  23. Nie, W., & He, D. (2010). A probability approach to anomaly detection with twin support vector machines. Journal of Shanghai Jiaotong University (Science), 15, 385–391.

    Article  Google Scholar 

  24. Aggarwal, S., Tomar, D., & Verma, S. (2014). Prediction of software defects using twin support vector machine. In IEEE International Conference on Information Systems and Computer Networks (ISCON) (pp. 128–132).

    Google Scholar 

  25. Ding, M., Yang, D., & Li, X. (2013). Fault diagnosis for wireless sensor by twin support vector machine. In Mathematical Problems in Engineering Cairo: Hindawi Publishing Corporation.

    Google Scholar 

  26. Chu, M., Gong, R., & Wang, A. (2014). Strip stee surface defect classification method based on enhanced twin support vector machine. ISIJ International (the Iron and Steel Institute of Japan), 54(1), 119–124.

    Article  Google Scholar 

  27. Shen, Z., Yao, N., Dong, H., & Yao, Y. (2014). Application of twin support vector machine for fault diagnosis of rolling bearing. In Mechatronics and Automatic Control Systems (pp. 161–167). Heidelberg: Springer.

    Google Scholar 

  28. Mozafari, K., Nasir, J., Charkar, N. M., & Jalili, S. (2011). Action recognition by local space-time features and least square twin SVM (LS-TSVM). First International IEEE Conference Informatics and Computational Intelligence (ICI) (pp. 287–292).

    Google Scholar 

  29. Mozafari, K., Nasiri, J. A., Charkari, N. M., & Jalili, S. (2011). Hierarchical least square twin support vector machines based framework for human action recognition. In 2011 9th Iranian Conference on Machine Vision and Image Processing (MVIP) (pp. 1-5). New York: IEEE.

    Google Scholar 

  30. Nasiri, J. A., Charkari, N. M., & Mozafari, K. (2014). Energy-based model of least squares twin support vector machines for human action recognition. Signal Processing, 104, 248–257.

    Article  Google Scholar 

  31. Khemchandani, R., & Sharma, S. (2016). Robust least squares twin support vector machine for human activity recognition. Applied Soft Computing.

    Google Scholar 

  32. Shao, Y.-H., Zhang, C.-H., Wang, X.-B., & Deng, N.-Y. (2011). Improvements on twin support vector machines. IEEE Transactions on Neural Networks, 22(6), 962–968.

    Article  Google Scholar 

  33. Golub, G. H., & Van Loan, C. F. (1996). Matrix computations (3rd ed.). Baltimore, Maryland: The John Hopkins University Press.

    MATH  Google Scholar 

  34. Khemchandani, R., & Saigal, P. (2015). Color image classification and retrieval through ternary decision structure based multi-category TWSVM. Neurocomputing, 165, 444–455.

    Article  Google Scholar 

  35. Pickard, R., Graszyk, C., Mann, S., Wachman, J., Pickard, L., & Campbell, L. (1995). Vistex database. In Media Lab Massachusetts: MIT Cambridge.

    Google Scholar 

  36. Yang, H. Y., Wang, X. Y., Niu, P. P., & Liu, Y. C. (2014). Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Networks, 57, 152–165.

    Article  Google Scholar 

  37. Ye, Y. F., Liu, X. J., Shao, Y. H., & Li, J. Y. (2013). L1-\(\varepsilon \)-Twin support vector regression for the determinants of inflation: A comparative study of two periods in China. Procedia Computer Science, 17, 514–522.

    Article  Google Scholar 

  38. Ye, Y., Shao, Y., & Chen, W. (2013). Comparing inflation forecasts using an \(\varepsilon \)-wavelet twin support vector regression. Journal of Information and Computational Science, 10, 2041–2049.

    Article  Google Scholar 

  39. Fang, S., & Hai Yang, S. (2013). A wavelet kernel-based primal twin support vector machine for economic development prediction. In Mathematical Problems in Engineering Hindwani Publishing Corporation.

    Google Scholar 

  40. Khemchandani, R., Jayadeva, Chandra, S. (2008). Incremental twin support vector machines. In S. K. Neogy, A. K. das, & R. B. Bapat, (Eds.), International Conference on Modeling, Computation and Optimization. Published in Modeling, Computation and Optimization, ICMCO-08 Singapore: World Scientific.

    Google Scholar 

  41. Hao, Y., Zhang, H., (2014). A fast incremental learning algorithm based on twin support vector machine. In Seventh International IEEE Symposium on Computational Intelligence and Design (ISCID) (vol. 2, pp. 92–95).

    Google Scholar 

  42. Mu, X., Chen, L., & Li, J. (2012). Online learning algorithm for least squares twin support vector machines. Computer Simulation, 3, 1–7.

    Google Scholar 

  43. Yang, J., & Liu, W. (2014). A structural twin parametric-margin support vector model and its application in students’ achievements analysis. Journal of Computational Information Systems, 10(6), 2233–2240.

    Google Scholar 

  44. Liu, W. (2014). Student achievement analysis based on weighted TSVM network. Journal of Computational Information Systems, 10(5), 1877–1883.

    Google Scholar 

  45. Liu, Z.-B., Gao, Y.-Y., & Wang, J.-Z. (2015). Automatic classification method of star spectra data based on manifold fuzzy twin support vector machine. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, 35(1), 263–266.

    MathSciNet  Google Scholar 

  46. Aldhaifallah, M., & Nisar, K. S. (2013). Identification of auto-regressive exogenous models based on twin support vector machine regression. Life Science Journal, 10(4).

    Google Scholar 

  47. Gao, S. B., Ding, J., & Zhang, Y. J. (2011). License plate location algorithm based on twin SVM. Advanced Materials Research (vol. 271, pp. 118-124). Switzerland: Trans Tech Publication.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (IIT), New Delhi, India

    Jayadeva

  2. Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University (SAU), New Delhi, India

    Reshma Khemchandani

  3. Department of Mathematics, Indian Institute of Technology (IIT), New Delhi, India

    Suresh Chandra

Authors
  1. Jayadeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reshma Khemchandani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suresh Chandra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reshma Khemchandani .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Jayadeva, Khemchandani, R., Chandra, S. (2017). Applications Based on TWSVM. In: Twin Support Vector Machines. Studies in Computational Intelligence, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-46186-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46186-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46184-7

  • Online ISBN: 978-3-319-46186-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation