{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T07:56:35Z","timestamp":1747209395040,"version":"3.40.5"},"reference-count":0,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"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":[[2016]]},"abstract":"<jats:p>Data reduction is a common pre-processing step for k-nearest neighbor\n   classification (kNN). The existing prototype selection methods implement\n   different criteria for selecting relevant points to use in classification,\n   which constitutes a selection bias. This study examines the nature of the\n   instance selection bias in intrinsically high-dimensional data. In\n   high-dimensional feature spaces, hubs are known to emerge as centers of\n   influence in kNN classification. These points dominate most kNN sets and are\n   often detrimental to classification performance. Our experiments reveal that\n   different instance selection strategies bias the predictions of the behavior\n   of hub-points in high-dimensional data in different ways. We propose to\n   introduce an intermediate un-biasing step when training the neighbor\n   occurrence models and we demonstrate promising improvements in various\n   hubness-aware classification methods, on a wide selection of high-dimensional\n   synthetic and real-world datasets.<\/jats:p>","DOI":"10.2298\/csis140929039t","type":"journal-article","created":{"date-parts":[[2015,10,13]],"date-time":"2015-10-13T01:11:26Z","timestamp":1444698686000},"page":"1-21","source":"Crossref","is-referenced-by-count":1,"title":["Correcting the hub occurrence prediction bias in many dimensions"],"prefix":"10.2298","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1624-0220","authenticated-orcid":false,"given":"Nenad","family":"Tomasev","sequence":"first","affiliation":[{"name":"Institute Jo\u017eef Stefan, Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krisztian","family":"Buza","sequence":"additional","affiliation":[{"name":"Institute of Genomic Medicine and Rare Disorders, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dunja","family":"Mladenic","sequence":"additional","affiliation":[{"name":"Institute Jo\u017eef Stefan, 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":"2023-05-29T08:32:33Z","timestamp":1685349153000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141500039T"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016]]}},"URL":"https:\/\/doi.org\/10.2298\/csis140929039t","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"type":"print","value":"1820-0214"},{"type":"electronic","value":"2406-1018"}],"subject":[],"published":{"date-parts":[[2016]]}}}