Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/151223
Title: | General twin support vector machine with pinball loss function | Authors: | Tanveer M. Sharma A. Suganthan, Ponnuthurai Nagaratnam |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2019 | Source: | Tanveer M., Sharma A. & Suganthan, P. N. (2019). General twin support vector machine with pinball loss function. Information Sciences, 494, 311-327. https://dx.doi.org/10.1016/j.ins.2019.04.032 | Journal: | Information Sciences | Abstract: | The standard twin support vector machine (TSVM) uses the hinge loss function which leads to noise sensitivity and instability. In this paper, we propose a novel general twin support vector machine with pinball loss (Pin-GTSVM) for solving classification problems. We show that the proposed Pin-GTSVM is noise insensitive and more stable for re-sampling. Further, the computational complexity of the proposed Pin-GTSVM is similar to that of the TSVM. Thus, the pinball loss function does not increase the computation time of the proposed Pin-GTSVM. Numerical experiments with different noise are performed on 17 UCI and KEEL benchmark real-world datasets and the results are compared with other baseline methods. The comparisons clearly show that the proposed Pin-GTSVM has better generalization performance for noise corrupted datasets. | URI: | https://hdl.handle.net/10356/151223 | ISSN: | 0020-0255 | DOI: | 10.1016/j.ins.2019.04.032 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2019 Elsevier Inc. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Journal Articles |
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