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Non-parallel bounded support matrix machine and its application in roller bearing fault diagnosis. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2022.12.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Nonparallel Support Vector Machine with L2-norm Loss and its DCD-type Solver. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11067-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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A non-convex robust small sphere and large margin support vector machine for imbalanced data classification. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07882-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
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4
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Ramp loss KNN-weighted multi-class twin support vector machine. Soft comput 2022. [DOI: 10.1007/s00500-022-07040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Zhang M, Zhang C, Liang X, Xia Z, Jian L, Nan J. A noise-resilient online learning algorithm with ramp loss for ordinal regression. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-205613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Ordinal regression has been widely used in applications, such as credit portfolio management, recommendation systems, and ecology, where the core task is to predict the labels on ordinal scales. Due to its learning efficiency, online ordinal regression using passive aggressive (PA) algorithms has gained a much attention for solving large-scale ranking problems. However, the PA method is sensitive to noise especially in the scenario of streaming data, where the ranking of data samples may change dramatically. In this paper, we propose a noise-resilient online learning algorithm using the Ramp loss function, called PA-RAMP, to improve the performance of PA method for noisy data streams. Also, we validate the order preservation of thresholds of the proposed algorithm. Experiments on real-world data sets demonstrate that the proposed noise-resilient online ordinal regression algorithm is more robust and efficient than state-of-the-art online ordinal regression algorithms.
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Affiliation(s)
- Maojun Zhang
- School of Business, Suzhou University of Science and Technology, Suzhou, Jiangsu, China
- School of Mathematics and Computer Science, Guilin University of Electronic Technology, Guilin, Guangxi, China
- School of Business, Suzhou University of Science and Technology, Suzhou, Jiangsu, China
| | - Cuiqing Zhang
- School of Mathematics and Computer Science, Guilin University of Electronic Technology, Guilin, Guangxi, China
- School of Business, Suzhou University of Science and Technology, Suzhou, Jiangsu, China
| | - Xijun Liang
- College of Science, China University of Petroleum, Qingdao, Shandong, China
| | - Zhonghang Xia
- School of Engineering and Applied Science, Western Kentucky University, Bowling Green, KY, USA
| | - Ling Jian
- School of Economics and Management, China University of Petroleum, Qingdao, Shandong, China
| | - Jiangxia Nan
- School of Business, Suzhou University of Science and Technology, Suzhou, Jiangsu, China
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Zhu J, Wang H, Li H, Zhang Q. Fast multi-view twin hypersphere support vector machine with consensus and complementary principles. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02986-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Reductive and effective discriminative information-based nonparallel support vector machine. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02874-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Borah P, Gupta D. Robust twin bounded support vector machines for outliers and imbalanced data. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01847-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Saigal P, Rastogi R, Chandra S. Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10323-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Alam S, Sonbhadra SK, Agarwal S, Nagabhushan P. One-class support vector classifiers: A survey. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105754] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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