Top

Cognitive Computation

Published in:

06-07-2024 | Research

Twin Bounded Support Vector Machine with Capped Pinball Loss

Authors: Huiru Wang, Xiaoqing Hong, Siyuan Zhang

Published in: Cognitive Computation | Issue 5/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In order to obtain a more robust and sparse classifier, in this paper, we propose a novel classifier termed as twin bounded support vector machine with capped pinball loss (CPin-TBSVM), which has the excellent properties of being insensitive to feature and label noise. Given that the proposed model is non-convex, we use the convex-concave procedure algorithm (CCCP) to solve a series of two smaller-sized quadratic programming problems to find the optimal solution. In the process of solving the iterative subproblem, the dual coordinate descent method (DCDM) is used for speeding up solving optimization problems. Moreover, we analyze its theoretical properties, including that the capped pinball loss satisfies Bayes’ rule and CPin-TBSVM has certain noise insensitivity and sparsity. The properties are verified on an artificial dataset as well. The numerical experiment is conducted on 24 UCI datasets and the results are compared with four other models which include SVM, TSVM, Pin-GTSVM and TPin-TSVM. The results show that the proposed CPin-TBSVM has a better classification effect and noise insensitivity.

previous article Effect of Leakage Delays on Bifurcation in Fractional-Order Bidirectional Associative Memory Neural Networks with Five Neurons and Discrete Delays

next article Quasi-projective Synchronization Control of Delayed Stochastic Quaternion-Valued Fuzzy Cellular Neural Networks with Mismatched Parameters

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

UCI: https://archive.ics.uci.edu/ml/index.php.

Vapnik VN. The nature of statistical learning theory. Berlin: Springer; 1995.CrossRef

Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. ACM Press. 1992;10(1145/130385): 130401.

Zhao S, Liu S, Qiu B, Hong Z, Zhao D, Dong L. Leak detection method of liquid-filled pipeline based on vmd and svm. Urban Water J. 2023;20:1169–82. https://doi.org/10.1080/1573062X.2023.2251952.CrossRef

Kong L, Zhao X. An improved predictor for identifying recombination spots based on support vector machine. J Comp Methods in Sci and Eng. 2023;23:2485–96. https://doi.org/10.3233/JCM-226872.CrossRef

Surucu O, Gadsden SA, Yawney J. Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Syst Appl. 2023;221: 119738. https://doi.org/10.1016/j.eswa.2023.119738.CrossRef

Hu L, Cui J. Digital image recognition based on fractional-order-pca-svm coupling algorithm. Measurement. 2019;145:150–9. https://doi.org/10.1016/j.measurement.2019.02.006.CrossRef

Liu Z, Zhang Q, Wang P, Li Z, Wang H. Automated classification of stems and leaves of potted plants based on point cloud data. Biosyst Eng. 2020;200:215–230. https://doi.org/10.48550/arXiv.2002.12536.

Jayadeva Khemchandani R, Chandra S. Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell. 2007;29:905–10. https://doi.org/10.1109/TPAMI.2007.1068.CrossRef

Peng X. Tpmsvm: A novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn. 2011;44:2678–92. https://doi.org/10.1016/j.patcog.2011.03.031.CrossRef

10.

Huang X, Shi L, Suykens JAK. Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell. 2014;36:984–97. https://doi.org/10.1109/TPAMI.2013.178.CrossRef

11.

Yang L, Dong H. Support vector machine with truncated pinball loss and its application in pattern recognition. Chemom Intell Lab Syst. 2018;177:89–99. https://doi.org/10.1016/j.chemolab.2018.04.003.CrossRef

12.

Huang X, Shi L, Suykens JAK. Ramp loss linear programming support vector machine. J Mach Learn Res. 2014;15:2185–211. https://doi.org/10.1016/j.procs.2016.05.432.MathSciNetCrossRef

13.

Shao YH, Zhang CH, Wang XB, Deng NY. Improvements on twin support vector machines. IEEE Trans Neural Netw. 2011;22:962–8. https://doi.org/10.1109/TNN.2011.2130540.CrossRef

14.

Prasad SC, Anagha P, Balasundaram S. Robust pinball twin bounded support vector machine for data classification. Neural Process Lett. 2023; pp 1131–1153. https://doi.org/10.1007/s11063-022-10930-6.

15.

Anagha P, Balasundaram S. On twin bounded support vector machine with pinball loss. In: Advanced Machine Intelligence and Signal Processing, Springer, 2022; pp 177–190

16.

Zhang J, Lai Z, Kong H, Shen L. Robust twin bounded support vector classifier with manifold regularization. IEEE Transactions on Cybernetics, 2022; pp 1–16. https://doi.org/10.1109/TCYB.2022.3160013.

17.

Xu Y, Yang Z, Pan X. A novel twin support-vector machine with pinball loss. IEEE Transactions on Neural Networks and Learning Systems. 2017;28:359–70. https://doi.org/10.1109/TNNLS.2015.2513006.MathSciNetCrossRef

18.

Zhang Q, Wang H, Yoon SW. A 1-norm regularized linear programming nonparallel hyperplane support vector machine for binary classification problems. Neurocomputing. 2020;376:141–52. https://doi.org/10.1016/j.neucom.2019.09.068.CrossRef

19.

Tanveer M, Sharma A, Suganthan PN. General twin support vector machine with pinball loss function. Inf Sci. 2019;494:311–27. https://doi.org/10.1016/j.ins.2019.04.032.MathSciNetCrossRef

20.

Wang H, Shao Y. Fast generalized ramp loss support vector machine for pattern classification. Pattern Recogn. 2024;146: 109987. https://doi.org/10.1016/j.patcog.2023.109987.CrossRef

21.

Liu M, Yang J, Li S, Zhou Z, Fan E, Zheng W. Robust gmm least square twin k-class support vector machine for urban water pipe leak recognition. Expert Syst Appl. 2022;195. https://doi.org/10.1016/j.eswa.2022.116525.

22.

Wang H, Liu Y, Zhang S. Smooth and semi-smooth pinball twin support vector machine. Expert Syst Appl. 2023;226: 120189. https://doi.org/10.1016/j.eswa.2023.120189.CrossRef

23.

Qi K, Yang H. Joint rescaled asymmetric least squared nonparallel support vector machine with a stochastic quasi-newton based algorithm. Appl Intell. 2022;52:14387–405. https://doi.org/10.1007/s10489-022-03183-2.CrossRef

24.

Liu M, Shao Y, Li C, Chen W. Smooth pinball loss nonparallel support vector machine for robust classification. Appl Soft Comput. 2021;98: 106840. https://doi.org/10.1016/j.asoc.2020.106840.CrossRef

25.

Wang H, Xu Y, Zhou Z. Twin-parametric margin support vector machine with truncated pinball loss. Neural Comput Appl. 2021;33:3781–98. https://doi.org/10.1007/s00521-020-05225-7.CrossRef

26.

Wang H, Li G, Wang Z. Fast svm classifier for large-scale classification problems. Inf Sci. 2023;642: 119136. https://doi.org/10.1016/j.ins.2023.119136.CrossRef

27.

Damminsed V, Panup W, Wangkeeree R. Laplacian twin support vector machine with pinball loss for semi-supervised classification. IEEE Access. 2023;11:31399–416. https://doi.org/10.1109/ACCESS.2023.3262270.CrossRef

28.

Makmuang D, Ratiphaphongthon W, Wangkeeree R. Smooth support vector machine with generalized pinball loss for pattern classification. J Supercomput. 2023;79:11684–706. https://doi.org/10.1007/s11227-023-05082-w.CrossRef

29.

Wang H, Lu S, Zhou Z. Ramp loss for twin multi-class support vector classification. Int J Syst Sci. 2020;51:1448–63. https://doi.org/10.1080/00207721.2020.1765047.MathSciNetCrossRef

30.

Pang X, Zhao J, Xu Y. A novel ramp loss-based multi-task twin support vector machine with multi-parameter safe acceleration. Neural Netw. 2022;150:194–212. https://doi.org/10.1016/j.neunet.2022.03.006.CrossRef

31.

Tang L, Tian Y, Li W, Pardalos PM. Valley-loss regular simplex support vector machine for robust multiclass classification. Knowl-Based Syst. 2021;216. https://doi.org/10.1016/j.knosys.2021.106801.

32.

Bartlett P, Jordan M, McAuliffe J. Convexity, classification, and risk bounds. J Am Stat Assoc. 2006;101:138–56. https://doi.org/10.1198/016214505000000907.MathSciNetCrossRef

33.

Yang Z, Xu Y. A safe accelerative approach for pinball support vector machine classifier. Knowl-Based Syst. 2018;147:12–24. https://doi.org/10.1016/j.knosys.2018.02.010.CrossRef

Title: Twin Bounded Support Vector Machine with Capped Pinball Loss
Authors: Huiru Wang
Xiaoqing Hong
Siyuan Zhang
Publication date: 06-07-2024
Publisher: Springer US
Published in: Cognitive Computation / Issue 5/2024
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-024-10307-y

Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization

Open Access
Research

Convolutionary, Evolutionary, and Revolutionary: What’s Next for Brains, Bodies, and AI?

Open Access

Federated Constrastive Learning and Visual Transformers for Personal Recommendation

Open Access
Research

The Duo of Visual Servoing and Deep Learning-Based Methods for Situation-Aware Disaster Management: A Comprehensive Review

Detection of Cardiovascular Diseases Using Data Mining Approaches: Application of an Ensemble-Based Model

Research

ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review

Open Access
Review

Springer Professional

Twin Bounded Support Vector Machine with Capped Pinball Loss

Abstract

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 5/2024

Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization

Convolutionary, Evolutionary, and Revolutionary: What’s Next for Brains, Bodies, and AI?

Federated Constrastive Learning and Visual Transformers for Personal Recommendation

The Duo of Visual Servoing and Deep Learning-Based Methods for Situation-Aware Disaster Management: A Comprehensive Review

Detection of Cardiovascular Diseases Using Data Mining Approaches: Application of an Ensemble-Based Model

ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review

Premium Partner

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization

Convolutionary, Evolutionary, and Revolutionary: What’s Next for Brains, Bodies, and AI?

Federated Constrastive Learning and Visual Transformers for Personal Recommendation

The Duo of Visual Servoing and Deep Learning-Based Methods for Situation-Aware Disaster Management: A Comprehensive Review

Detection of Cardiovascular Diseases Using Data Mining Approaches: Application of an Ensemble-Based Model

ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review