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Moosaei H, Ganaie M, Hladík M, Tanveer M. Inverse free reduced universum twin support vector machine for imbalanced data classification. Neural Netw 2023; 157:125-135. [DOI: 10.1016/j.neunet.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 11/09/2022]
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Naik B, Nayak J, Dash PB. Higher order ANN parameter optimization using hybrid opposition-elitism based metaheuristic. Evol Intel 2022; 15:2055-2075. [DOI: 10.1007/s12065-021-00610-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Panup W, Ratipapongton W, Wangkeeree R. A Novel Twin Support Vector Machine with Generalized Pinball Loss Function for Pattern Classification. Symmetry (Basel) 2022; 14:289. [DOI: 10.3390/sym14020289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
We introduce a novel twin support vector machine with the generalized pinball loss function (GPin-TSVM) for solving data classification problems that are less sensitive to noise and preserve the sparsity of the solution. In addition, we use a symmetric kernel trick to enlarge GPin-TSVM to nonlinear classification problems. The developed approach is tested on numerous UCI benchmark datasets, as well as synthetic datasets in the experiments. The comparisons demonstrate that our proposed algorithm outperforms existing classifiers in terms of accuracy. Furthermore, this employed approach in handwritten digit recognition applications is examined, and the automatic feature extractor employs a convolution neural network.
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Pan H, Yang Y, Wang P, Wang J, Cheng J. Symplectic incremental matrix machine and its application in roller bearing condition monitoring. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106566] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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