{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:30:24Z","timestamp":1760596224603,"version":"3.40.5"},"reference-count":0,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2012,1,1]],"date-time":"2012-01-01T00:00:00Z","timestamp":1325376000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2012]]},"abstract":"<jats:p>Hubness is a recently described aspect of the curse of dimensionality\n   inherent to nearest-neighbor methods. This paper describes a new approach for\n   exploiting the hubness phenomenon in k-nearest neighbor classification. We\n   argue that some of the neighbor occurrences carry more information than\n   others, by the virtue of being less frequent events. This observation is\n   related to the hubness phenomenon and we explore how it affects\n   high-dimensional k-nearest neighbor classification. We propose a new\n   algorithm, Hubness Information k-Nearest Neighbor (HIKNN), which introduces\n   the k-occurrence informativeness into the hubness-aware k-nearest neighbor\n   voting framework. The algorithm successfully overcomes some of the issues\n   with the previous hubness-aware approaches, which is shown by performing an\n   extensive evaluation on several types of high-dimensional data.<\/jats:p>","DOI":"10.2298\/csis111211014t","type":"journal-article","created":{"date-parts":[[2012,6,7]],"date-time":"2012-06-07T09:56:21Z","timestamp":1339062981000},"page":"691-712","source":"Crossref","is-referenced-by-count":31,"title":["Nearest neighbor voting in high dimensional data: Learning from past occurrences"],"prefix":"10.2298","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1624-0220","authenticated-orcid":false,"given":"Nenad","family":"Tomasev","sequence":"first","affiliation":[{"name":"Artificial Intelligence Laboratory, Jo\u017eef Stefan Institute and Jo\u017eef Stefan International Postgraduate School, Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dunja","family":"Mladenic","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Laboratory, Jo\u017eef Stefan Institute and Jo\u017eef Stefan International Postgraduate School, Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:30:37Z","timestamp":1685349037000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141200014T"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2012]]}},"URL":"https:\/\/doi.org\/10.2298\/csis111211014t","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"type":"print","value":"1820-0214"},{"type":"electronic","value":"2406-1018"}],"subject":[],"published":{"date-parts":[[2012]]}}}