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Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

  • S.I. : Healthcare Analytics
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Abstract

This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The proposed FLTPMSVM assigns the weights to each data sample on the basis of fuzzy membership values to reduce the effect of outliers. This paper also adopts the square of the 2-norm of slack variables to make the objective function more convex. The proposed FLTPMSVM solves simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems. No external toolbox is required for the proposed FLTPMSVM as compared to the other methods. To establish the applicability of the proposed FLTPMSVM in the area of biomedical data classification, numerical experiments are performed on several biomedical datasets. The proposed FLTPMSVM gives an improved generalization performance and reduced training cost as compared to support vector machine (SVM), twin support vector machine (TWSVM), fuzzy twin support vector machine (FTSVM), twin parametric-margin support vector machine (TPMSVM) and new fuzzy twin support vector machine (NFTSVM).

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References

  1. Scholkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(8):1207–1245

    Article  Google Scholar 

  2. Balasundaram S, Gupta D, Prasad SC (2017) A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization. Appl Intell 46(1):124–134

    Article  Google Scholar 

  3. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(6):273–297

    MATH  Google Scholar 

  4. Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(5):464–471

    Google Scholar 

  5. Chaudhuri KD (2010) Fuzzy Support Vector Machine for Bankruptcy Prediction. Appl Soft Comput 11(5):2472–2486

    Google Scholar 

  6. Tsujinishi D, Abe S (2003) Fuzzy Least Squares Support Vector Machines. Proc Int Joint Conf Neural Netw, Portland, Oregon, pp. 1599–1604

  7. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(6):293–300

    Article  Google Scholar 

  8. Jayadeva RK, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(8):905–910

    Article  Google Scholar 

  9. Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Mach Learn Res 161–177

  10. Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  11. Mangasarian OL (1994) Nonlinear programming. SIAM, Philadelphia

    Book  Google Scholar 

  12. Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Mach Learn Res 1:161–177

    MathSciNet  MATH  Google Scholar 

  13. Murphy PM, Aha DW (1992) UCI Repository of Machine Learning Databases. University of California, Irvine. https://archive.ics.uci.edu/ml/datasets.php

  14. Hao PY (2010) New support vector algorithms with parametric insensitive/margin model. Neural Netw 23(4):60–73

    Article  Google Scholar 

  15. Pen X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn 44:2678–2692

    Article  Google Scholar 

  16. Batuwita R, Palade V (2010) FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Trans Fuzzy Syst 18(3):558–571

    Article  Google Scholar 

  17. Khemchandani R, Sharma S (2016) Robust least squares twin support vector machine for human activity recognition. Appl Soft Comput 47:33–46

    Article  Google Scholar 

  18. Malhotra R, Malhotra DK (2003) Evaluating consumer loans using neural networks. Omega 31:83–96

    Article  Google Scholar 

  19. Zhang S, Zhao S, Sui Y, Zhang L (2015) Single object tracking with fuzzy least squares support vector machine. IEEE Trans Image Process 24:5723–5738

    Article  MathSciNet  Google Scholar 

  20. Ebrahimi T, Garcia GN, Vesin JM (2003) Joint time-frequency-space classification of EEG in a brain-computer interface application. J Appl Signal Process 1(10):713–729

    MATH  Google Scholar 

  21. Joachims T, Ndellec C, Rouveriol C (1998) Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: European Conference on Machine Learning No.10, Chemnitz, Germany, pp.137–142

  22. Vapnik VN (1998) Statistical Learning Theory. John Wiley & Sons, New York

    MATH  Google Scholar 

  23. Bao YK, Liu ZT, Guo L, Wang W (2005) Forecasting stock composite index by fuzzy support vector machines regression. Proc Int Conf Mach Learn Cybern 6:3535–3540

    Google Scholar 

  24. Wang Y, Wang S, Lai KK (2005) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(9):820–831

    Article  Google Scholar 

  25. Zhou J, Chan KL, Chong VFH, Krishnan FM (2006) Extraction of Brain Tumor from MR Images using One-class Support Vector Machine. In: Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. IEEE

  26. Kazama J, Makino T, Ohta Y, Tsujii J (2002) Tuning Support Vector Machines for Biomedical Named Entity Recognition. In: Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain-Volume 3. Association for Computational Linguistics

  27. Zhang Y, Dong Z, Aijun A, Wang S, Ji G, Zhang Z, Yang J (2015) Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J Med Imaging Health Inform 5(7):1395–1403

    Article  Google Scholar 

  28. Zhang YD, Wang SH, Yang XJ, Dong ZC, Liu G, Phillips P, Yuan TF (2015) Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus 4(1):716

    Article  Google Scholar 

  29. Li D, Zhang H, Khan MS, Mi F (2018) A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition. Biomed Signal Process Control 41:222–232

    Article  Google Scholar 

  30. Hagiwara Y, Fujita H, Lih OS, Hong TJ, San TR, Ciaccio EJ, Acharya UR (2018) Computer-aided diagnosis of atrial fibrillation based on ECG Signals: a review. Inf Sci 467:99–114

    Article  Google Scholar 

  31. Ramírez J, Górriz JM, Gonzalez DS, Romero A, López M, Álvarez L, Río MG (2013) Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Inf Sci 237:59–72

    Article  Google Scholar 

  32. Muhammad T, Khan A (2016) Protein subcellular localization of fluorescence microscopy images: employing new statistical and Texton based image features and SVM based ensemble classification. Inf Sci 345:65–80

    Article  Google Scholar 

  33. Bo J, Tang YC, Zhang YQ (2007) Support vector machines with genetic fuzzy feature transformation for biomedical data classification. Inf Sci 177(2):476–489

    Article  Google Scholar 

  34. Liu Y, Xu Z, Li C (2018) Distributed Online Semi-Supervised Support Vector Machine. Inform Sci 466: 236–257.

  35. Wang Z, Shao YH, Bai L, Li CN, Liu LM, Deng NY (2018) Insensitive stochastic gradient twin support vector machines for large scale problems. Inf Sci 462:114–131

    Article  MathSciNet  Google Scholar 

  36. Calma TR, Sick B (2018) Semi-supervised active learning for support vector machines: a novel approach that exploits structure information in data. Inform Sci 456:13–33

    Article  MathSciNet  Google Scholar 

  37. Li Z, Zhou WD (2016) Fisher-regularized support vector machine. Inf Sci 343:79–93

    MathSciNet  MATH  Google Scholar 

  38. Nguyen T, Khosravi A, Creighton D, Nahavandi S (2015) Hidden Models for cancer classification using gene expression profiles. Inf Sci 316:293–307

    Article  Google Scholar 

  39. Phan N, Dou D, Wang H, Kil D, Piniewski B (2017) Ontology-based deep learning for human behavior prediction with explanations in health social networks. Inf Sci 384:298–313

    Article  Google Scholar 

  40. Wang Y, Zhao Q, Wang B, Wang S, Zhang Y, Guo W, Feng Z (2016) A real-time active pedestrian tracking system inspired by the human visual system. Cogn Comput 8(1):39–51

    Article  Google Scholar 

  41. Gupta V, Kaur N (2016) A novel hybrid text summarization system for Punjabi text. Cogn Comput 8(2):261–277

    Article  Google Scholar 

  42. Casals JS, Caiafa CF, Zhao Q, Cichocki A (2018) Brain-computer interface with corrupted EEG data: a tensor completion approach. Cogn Comput 10(6):1062–1074

    Article  Google Scholar 

  43. Hazarika BB, Gupta D, Berlin M (2020) A Comparative Analysis of Artificial Neural Network and Support Vector Regression for River Suspended Sediment Load Prediction. First International Conference on Sustainable Technologies for Computational Intelligence. Springer, Singapore, pp 339–349

    Chapter  Google Scholar 

  44. Keller JM, Hunt DJ (1985) Incorporating fuzzy membership functions into the perceptron algorithm. IEEE Trans Pattern Anal Mach Intell 6:693–699

    Article  Google Scholar 

  45. Hazarika BB, Gupta D (2020) Density-weighted support vector machines for binary class imbalance learning. In: Neural Computing and Applications 1–19

  46. Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT (2020) Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review. In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

  47. Thapa S, Adhikari S, Naseem U, Singh P, Bharathy G, Prasad M (2020) Detecting Alzheimer’s Disease by Exploiting Linguistic Information from Nepali Transcript. In: International Conference on Neural Information Processing

  48. Thapa S, Singh P, Jain DK, Bharill N, Gupta A, Prasad M (2020) Data-driven approach based on feature selection technique for early diagnosis of Alzheimer’s disease. In: 2020 International Joint Conference on Neural Networks (IJCNN)

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Funding

This work was fully supported by the Science and Engineering Research Board, Government of India (SERB), under early career research award ECR/2016/001464 and TEQIP-III seed grant project no: SG-TEQIP-III/CSE/DG.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Yupia, Arunachal Pradesh, India

    Deepak Gupta & Usha Mary Sharma

  2. Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Parashjyoti Borah

  3. School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia

    Mukesh Prasad

Authors
  1. Deepak Gupta
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  2. Parashjyoti Borah
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  3. Usha Mary Sharma
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  4. Mukesh Prasad
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Correspondence to Mukesh Prasad.

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Gupta, D., Borah, P., Sharma, U.M. et al. Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis. Neural Comput & Applic 34, 11335–11345 (2022). https://doi.org/10.1007/s00521-021-05866-2

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