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|Title:||General twin support vector machine with pinball loss function||Authors:||Tanveer M.
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||Rights:||© 2019 Elsevier Inc. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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