TSVMPath: Fast Regularization Parameter Tuning Algorithm for Twin Support Vector Machine
Twin support vector machine (TSVM) has attracted much attention in the field of machine learning with good generalization ability and computational performance. However, the conventional grid search method is very time-consuming to obtain the optimal regularization parameter. To address this problem...
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| Published in: | Neural processing letters Vol. 54; no. 6; pp. 5457 - 5482 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer US
01.12.2022
Springer Nature B.V |
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| ISSN: | 1370-4621, 1573-773X |
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| Abstract | Twin support vector machine (TSVM) has attracted much attention in the field of machine learning with good generalization ability and computational performance. However, the conventional grid search method is very time-consuming to obtain the optimal regularization parameter. To address this problem, we develop a novel fast regularization parameter tuning algorithm for TSVM, named TSVMPath. After transforming the models of two sub-optimization problems, we divide the two classes of samples into different sets. Lagrangian multipliers are then proved to be piecewise linear concerning the corresponding regularization parameters, greatly extending the search space of the solution. By proving that the Lagrangian multipliers of two sub-optimization models are 1 when the regularization parameters approach infinity, we design a simple yet effective initialization. As a result, the entirely regularized solution path can be obtained without solving quadratic programming problems. Four types of events are finally defined to update the solution path. Experiments on 8 UCI datasets show that the prediction accuracy of TSVMPath is superior to the best competing methods, with up to four orders of magnitude speed-up for the computational overhead compared with the grid search method. |
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| AbstractList | Twin support vector machine (TSVM) has attracted much attention in the field of machine learning with good generalization ability and computational performance. However, the conventional grid search method is very time-consuming to obtain the optimal regularization parameter. To address this problem, we develop a novel fast regularization parameter tuning algorithm for TSVM, named TSVMPath. After transforming the models of two sub-optimization problems, we divide the two classes of samples into different sets. Lagrangian multipliers are then proved to be piecewise linear concerning the corresponding regularization parameters, greatly extending the search space of the solution. By proving that the Lagrangian multipliers of two sub-optimization models are 1 when the regularization parameters approach infinity, we design a simple yet effective initialization. As a result, the entirely regularized solution path can be obtained without solving quadratic programming problems. Four types of events are finally defined to update the solution path. Experiments on 8 UCI datasets show that the prediction accuracy of TSVMPath is superior to the best competing methods, with up to four orders of magnitude speed-up for the computational overhead compared with the grid search method. |
| Author | Li, Juntao Zhang, Qiyang Zhou, Kanglei |
| Author_xml | – sequence: 1 givenname: Kanglei surname: Zhou fullname: Zhou, Kanglei organization: School of Computer Science and Engineering, Beihang University – sequence: 2 givenname: Qiyang surname: Zhang fullname: Zhang, Qiyang organization: State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications – sequence: 3 givenname: Juntao orcidid: 0000-0002-3288-4395 surname: Li fullname: Li, Juntao email: lijuntao@htu.edu.cn organization: College of Mathematics and Information Science, Henan Normal University |
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| Keywords | Statistical machine learning Regularized solution path Parameter tuning algorithm Twin support vector machine |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Classification Complex Systems Computational Intelligence Computer Science Design Efficiency Lagrange multiplier Machine learning Optimization Optimization models Parameters Quadratic programming Regularization Search methods Support vector machines Tuning |
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