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In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS\u2010NSVR, which first attempts to solve the nonlinear feature selection problem in the regression technology field. FS\u2010NSVR preserves the representative features selected in the complex nonlinear system due to its use of a feature selection matrix in the original space. FS\u2010NSVR is a challenging mixed\u2010integer programming problem that is solved efficiently by using an alternate iterative greedy algorithm. Experimental results on three artificial datasets and five real\u2010world datasets confirm that FS\u2010NSVR effectively selects representative features and discards redundant features in a nonlinear system. FS\u2010NSVR outperforms L\n                    <jats:sub>1<\/jats:sub>\n                    \u2010norm support vector regression, L\n                    <jats:sub>1<\/jats:sub>\n                    \u2010norm least squares support vector regression, and L\n                    <jats:sub>p<\/jats:sub>\n                    \u2010norm support vector regression on both feature selection ability and regression efficiency.\n                  <\/jats:p>","DOI":"10.1155\/2022\/4740173","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T12:51:45Z","timestamp":1669899105000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Novel Feature Selection Method for Nonlinear Support Vector Regression"],"prefix":"10.1155","volume":"2022","author":[{"given":"Kejia","family":"Xu","sequence":"first","affiliation":[]},{"given":"Ying","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6000-8635","authenticated-orcid":false,"given":"Yafen","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Weijie","family":"Chen","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113429"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1315"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2018.2789451"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aau4996"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10668-021-01724-2"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7670"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/918954"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.01.003"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4032628"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2937751"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2017.2647904"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3418284"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmm.2018.2877127"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2019.2910721"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2019.2933499"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2020.2977956"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2017.01.062"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0004-3702(97)00063-5"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1162\/153244303322753643"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1080\/1062936x.2017.1326402"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1080\/1062936x.2018.1491414"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijcsm.2021.114185"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2020.104196"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/cem.3311"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1897\/1\/012057"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.02.010"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.20965\/jaciii.2017.p1017"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2013.05.341"},{"key":"e_1_2_9_30_2","volume-title":"Theoretical Advances in Robust Optimization, Feature Selection, and Biomarker Discovery","author":"Guzman Y. 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