{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T08:43:51Z","timestamp":1775465031146,"version":"3.50.1"},"reference-count":40,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p>Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets.<\/jats:p>","DOI":"10.1142\/s0129065721500209","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T15:51:30Z","timestamp":1617205890000},"page":"2150020","source":"Crossref","is-referenced-by-count":6,"title":["Effective Automatic Method Selection for Nonlinear Regression Modeling"],"prefix":"10.1142","volume":"31","author":[{"given":"Jan","family":"Kalina","sequence":"first","affiliation":[{"name":"The Czech Academy of Sciences, Institute of Computer Science, Pod Vod\u00e1renskou v\u011b\u017e\u00ed 2, 182 07 Prague 8, Czech Republic"},{"name":"Charles University, Faculty of Mathematics and Physics, Sokolovsk\u00e1 83, 186 75 Prague 8, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ale\u0161","family":"Neoral","sequence":"additional","affiliation":[{"name":"The Czech Academy of Sciences, Institute of Computer Science, Pod Vod\u00e1renskou v\u011b\u017e\u00ed 2, 182 07 Prague 8, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petra","family":"Vidnerov\u00e1","sequence":"additional","affiliation":[{"name":"The Czech Academy of Sciences, Institute of Computer Science, Pod Vod\u00e1renskou v\u011b\u017e\u00ed 2, 182 07 Prague 8, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,3,29]]},"reference":[{"key":"S0129065721500209BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065719500242"},{"key":"S0129065721500209BIB002","doi-asserted-by":"publisher","DOI":"10.1142\/S012906572050032X"},{"key":"S0129065721500209BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5692-y"},{"key":"S0129065721500209BIB004","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00242"},{"key":"S0129065721500209BIB005","doi-asserted-by":"publisher","DOI":"10.5772\/36710"},{"key":"S0129065721500209BIB006","doi-asserted-by":"publisher","DOI":"10.1007\/s40435-020-00708-w"},{"key":"S0129065721500209BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-017-9328-y"},{"key":"S0129065721500209BIB008","doi-asserted-by":"publisher","DOI":"10.1145\/3377930.3390185"},{"key":"S0129065721500209BIB009","doi-asserted-by":"publisher","DOI":"10.1007\/s13721-016-0125-6"},{"key":"S0129065721500209BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2013.11.015"},{"key":"S0129065721500209BIB011","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73263-1"},{"key":"S0129065721500209BIB012","volume-title":"Nonlinear Regression","author":"Seber G. A. F.","year":"2003"},{"key":"S0129065721500209BIB013","doi-asserted-by":"publisher","DOI":"10.1080\/03610918.2010.490316"},{"key":"S0129065721500209BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2011.09.031"},{"key":"S0129065721500209BIB015","volume-title":"Robust Nonlinear Regression with Applications Using R.","author":"Riazoshams H.","year":"2019"},{"key":"S0129065721500209BIB016","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500604"},{"key":"S0129065721500209BIB017","first-page":"499","volume-title":"EANN 2020, Proc. Int. Neural Networks Society","volume":"2","author":"Kalina J.","year":"2020"},{"key":"S0129065721500209BIB018","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_10"},{"key":"S0129065721500209BIB019","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1992.10476254"},{"key":"S0129065721500209BIB020","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176350366"},{"key":"S0129065721500209BIB021","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/01621459.1993.10594315","volume":"88","author":"Stromberg A. J.","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"S0129065721500209BIB022","doi-asserted-by":"publisher","DOI":"10.1201\/b21993"},{"key":"S0129065721500209BIB023","series-title":"IMS Collections","first-page":"254","volume-title":"Nonparametrics and Robustness in Modern Statistical Inference and Time Series Analysis: A Festschrift in Honor of Professor Jana Jure\u010dkov\u00e1","volume":"7","author":"V\u00ed\u0161ek J. \u00c1.","year":"2010"},{"key":"S0129065721500209BIB024","first-page":"179","volume":"47","author":"V\u00ed\u0161ek J. \u00c1.","year":"2011","journal-title":"Kybernetika"},{"key":"S0129065721500209BIB025","doi-asserted-by":"publisher","DOI":"10.2478\/msr-2020-0002"},{"key":"S0129065721500209BIB026","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1524"},{"key":"S0129065721500209BIB027","author":"Kalina J.","year":"2021","journal-title":"Commun. Stat.-Simul. C"},{"key":"S0129065721500209BIB028","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2018.04.005"},{"key":"S0129065721500209BIB029","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-005-0024-4"},{"key":"S0129065721500209BIB031","volume-title":"Methodology in Robust and Nonparametric Statistics","author":"Jure\u010dkov\u00e1 J.","year":"2013"},{"key":"S0129065721500209BIB032","volume-title":"Robust Regression and Outlier Detection","author":"Rousseeuw P. J.","year":"1986"},{"key":"S0129065721500209BIB033","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.077"},{"key":"S0129065721500209BIB034","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"S0129065721500209BIB035","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.05.028"},{"key":"S0129065721500209BIB036","doi-asserted-by":"publisher","DOI":"10.1021\/ci034160g"},{"key":"S0129065721500209BIB037","first-page":"1485","volume":"10","author":"Xu H.","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"S0129065721500209BIB038","first-page":"713","volume":"11","author":"Shi J.","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"S0129065721500209BIB039","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-018-1154-8"},{"key":"S0129065721500209BIB040","doi-asserted-by":"publisher","DOI":"10.3233\/ICA-2010-0345"},{"key":"S0129065721500209BIB041","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04359-7"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065721500209","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T06:58:14Z","timestamp":1724741894000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065721500209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,29]]},"references-count":40,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["10.1142\/S0129065721500209"],"URL":"https:\/\/doi.org\/10.1142\/s0129065721500209","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,29]]}}}