Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (01): 79-85.doi: 10.12052/gdutxb.220027

• Computer Science and Technology • Previous Articles     Next Articles

A Least Squares Twin Support Vector Machine Method with Uncertain Data

Liu Jin-neng1, Xiao Yan-shan1, Liu Bo2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-02-21 Online:2024-01-25 Published:2024-02-01

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Abstract: Twin support vector machine learns two nonparallel hyperplanes by calculating two quadratic programming problems to solve the binary classification problems. However, in practical applications, the data usually contain uncertain information, making it difficult to construct the classification model. This paper proposed a new and efficient uncertain-data-based least squares twin support vector machine (ULSTSVM) method to address the problem of data uncertainty. Firstly, since the data may contain uncertain information, a noise vector was introduced to model the uncertain information of each example. Secondly, the noise vectors were incorporated into the least squares TWSVM. Finally, to solve the derived learning problem, we employed a two-step heuristic framework to train the least squares TWSVM classifier and updated the noise vectors alternatively. The experiments showed that our proposed ULSTSVM outperforms the baselines in training time and meanwhile achieves comparable classification accuracy. In sum, ULSTSVM adopts a noise vector to model the uncertain information and transforms the quadratic programming problems of TWSVM into linear equations, such that better classification accuracy and higher training efficiency can be obtained.

Key words: least squares, twin support vector machine, nonparallel plane learning, data uncertainty, classification

CLC Number: 

  • TP391
[1] CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning, 1995, 20(3): 273-297.
[2] LI S, KWOK J T, ZHU H, et al. Texture classification using the support vector machines [J]. Pattern Recognition, 2003, 36(12): 2883-2893.
[3] KHAN N M, KSANTITI R, AHMAD I S, et al. A novel SVM+NDA model for classification with an application to face recognition [J]. Pattern Recognition, 2012, 45(1): 66-79.
[4] BILAL M. Algorithmic optimisation of histogram intersection kernel support vector machine-based pedestrian detection using low complexity features [J]. IET Computer Vision, 2017, 11(5): 350-357.
[5] YUAN L, YAO E, TAN G. Automated and precise event detection method for big data in biomedical imaging with support vector machine [J]. International Journal of Computer Systems Science & Engineering, 2018, 33(2): 105-113.
[6] MANGASARIAN O L, WILD E W. Multisurface proximal support vector machine classification via generalized eigenvalues [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 28(1): 69-74.
[7] KHEMCHANDANI R, CHANDRA S. Twin support vector machines for pattern classification [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910.
[8] CHEN X, YANG J, YE Q, et al. Recursive projection twin support vector machine via within-class variance minimization [J]. Pattern Recognition, 2011, 44(10-11): 2643-2655.
[9] SHAO Y H, ZHANG C H, WANG X B, et al. Improvements on twin support vector machines [J]. IEEE Transactions on Neural Networks, 2011, 22(6): 962-968.
[10] KUMAR M A, GOPAL M. Least squares twin support vector machines for pattern classification [J]. Expert Systems with Applications, 2009, 36(4): 7535-7543.
[11] KUMAR M A, KHEMCHANDANI R, GOPAL M, et al. Knowledge based least squares twin support vector machines [J]. Information Sciences, 2010, 180(23): 4606-4618.
[12] TANVEER M, SHARMA S, MUHAMMAD K. Large-scale least squares twin svms [J]. ACM Transactions on Internet Technology (TOIT) , 2021, 21(2): 1-19.
[13] CHEN S G, WU X J, XU J. Locality preserving projection least squares twin support vector machine for pattern classification [J]. Pattern Analysis and Applications, 2020, 23(1): 1-13.
[14] QI Z, TIAN Y, SHI Y. Robust twin support vector machine for pattern classification [J]. Pattern Recognition, 2013, 46(1): 305-316.
[15] MAIRAL J, BACH F, PONCE J, et al. Online dictionary learning for sparse coding[C]//Proceedings of the 26th Annual International Conference on Machine Learning. New York: ACM, 2009: 689-696.
[16] ZHAO B, WANG F, ZHANG C. Efficient multiclass maximum margin clustering[C]//Proceedings of the 25th International Conference on Machine Learning. New York: ACM, 2008: 1248-1255.
[17] LIU B, XIAO Y, CAO L, et al. One-class-based uncertain data stream learning[C]//Proceedings of the 2011 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. Philadelphia: SIAM, 2011: 992-1003.
[18] BI J, ZHANG T. Support vector classification with input data uncertainty[C]//Neural Information Processing Systems. Cambridge: MIT Press, 2004: 161-168.
[19] TSANG S, KAO B, YIP K Y, et al. Decision trees for uncertain data [J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 23(1): 64-78.
[20] REN J, LEE S D, CHEN X, et al. Naive bayes classification of uncertain data[C]//2009 Ninth IEEE International Conference on Data Mining. Piscataway: IEEE, 2009: 944-949.
[21] GAO C, WANG J. Direct mining of discriminative patterns for classifying uncertain data[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2010: 861-870.
[22] YIN J, YANG Q, PAN J J. Sensor-based abnormal human-activity detection [J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(8): 1082-1090.
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