{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:52:51Z","timestamp":1777704771640,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T00:00:00Z","timestamp":1588896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,7,17]]},"abstract":"<jats:p>\n                    In this paper, we propose a nonparallel support vector machine with pinball loss (Pin-NPSVM) that deals with the noise sensitivity and resampling instability of NPSVM. More specifically, we redefine a pinball loss funtion and build a pair of quantile hyper-planes. Each quantile hyper-plane is constructed by using the new pinball loss instead of\n                    <jats:italic>\u025b<\/jats:italic>\n                    -insensitive loss, which makes the new classification model be insensitive to noise samples, especially for feature noise samples around the decision boundary. Moreover, instead of hinge loss, Pin-NPSVM also builds a pair of decision boundaries based on traditional pinball loss, which further improves the anti-nosie ability of the classification model. In a word, Pin-NPSVM not only inherits the characteristics of the nonparallel optimal hyper-planes, but also has a consistent model with Pin-SVM, which can process noise data well. Finally, numerical experimental results show that the Pin-NPSVM has more obvious advantages than other models in classification performance, especially for noise datasets.\n                  <\/jats:p>","DOI":"10.3233\/jifs-191845","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T14:48:30Z","timestamp":1588949310000},"page":"911-923","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A nonparallel support vector machine with pinball loss for pattern classification"],"prefix":"10.1177","volume":"39","author":[{"given":"Liming","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maoxiang","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongfen","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,5,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.05.012"},{"issue":"10","key":"e_1_3_2_3_2","first-page":"1025","article-title":"Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation","volume":"37","author":"Daga S.","year":"2017","unstructured":"DagaS., ShaikhinaT., LoweD., BriggsD. and KhovanvoaN., Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation, Biomedical Signal Processing & Control 37(10) (2017), 1025\u20131042.","journal-title":"Biomedical Signal Processing & Control"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/AICI.2010.82"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00089-8"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.12.013"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2362116"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_2_9_2","unstructured":"VapnikV.N. 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