{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T20:42:26Z","timestamp":1771015346690,"version":"3.50.1"},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2003,12]]},"abstract":"<jats:p> In this paper, we propose a thorough investigation of a nearest neighbor rule which we call the \"Symmetric Nearest Neighbor (sNN) rule\". Basically, it symmetrises the classical nearest neighbor relationship from which are computed the points voting for some instances. Experiments on 29 datasets, most of which are readily available, show that the method significantly outperforms the traditional Nearest Neighbors methods. Experiments on a domain of interest related to tropical pollution normalization also show the greater potential of this method. We finally discuss the reasons for the rule's efficiency, provide methods for speeding-up the classification time, and derive from the sNN rule a reliable and fast algorithm to fix the parameter k in the k-NN rule, a longstanding problem in this field. <\/jats:p>","DOI":"10.1142\/s0218001403002952","type":"journal-article","created":{"date-parts":[[2003,12,11]],"date-time":"2003-12-11T04:24:06Z","timestamp":1071116646000},"page":"1369-1382","source":"Crossref","is-referenced-by-count":19,"title":["A SIMPLE LOCALLY ADAPTIVE NEAREST NEIGHBOR RULE WITH APPLICATION TO POLLUTION FORECASTING"],"prefix":"10.1142","volume":"17","author":[{"given":"RICHARD","family":"NOCK","sequence":"first","affiliation":[{"name":"Grimaag-D\u00e9partement Scientifique Interfacultaire, Universit\u00e9 des Antilles-Guyane, Campus de Schoelcher, BP 7209, 97275 Schoelcher, France"}]},{"given":"MARC","family":"SEBBAN","sequence":"additional","affiliation":[{"name":"Eurise-D\u00e9partement d'Informatique,  Universit\u00e9 Jean Monnet, 23, Rue du Docteur Paul Michelon, 42023  Saint-Etienne Cedex 2, France"}]},{"given":"DIDIER","family":"BERNARD","sequence":"additional","affiliation":[{"name":"Laboratoire de Physique de l'Atmosph\u00e8re Tropicale, Universit\u00e9 des Antilles-Guyane, Campus de Fouillole, 97159 Pointe-\u00c0-Pitre, France"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/0025-326X(95)00085-2"},{"key":"rf3","first-page":"75","volume":"8","author":"Buntine W.","journal-title":"Mach. Learn."},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1145\/355744.355745"},{"key":"rf9","first-page":"1000","volume":"24","author":"Friedman J. H.","journal-title":"IEEE Trans. Comput."},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1972.1054809"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054155"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(87)90002-X"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1109\/34.506411"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(00)00137-9"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001403002952","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T22:20:57Z","timestamp":1565130057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001403002952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,12]]},"references-count":10,"journal-issue":{"issue":"08","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2003,12]]}},"alternative-id":["10.1142\/S0218001403002952"],"URL":"https:\/\/doi.org\/10.1142\/s0218001403002952","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,12]]}}}