Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/13756
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tomašev N. | en |
dc.contributor.author | Radovanović M. | en |
dc.contributor.author | Mladenić D. | en |
dc.contributor.author | Ivanović, Mirjana | en |
dc.date.accessioned | 2020-03-03T14:53:37Z | - |
dc.date.available | 2020-03-03T14:53:37Z | - |
dc.date.issued | 2011-09-07 | en |
dc.identifier.isbn | 9783642231988 | en |
dc.identifier.issn | 03029743 | en |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/13756 | - |
dc.description.abstract | High-dimensional data are by their very nature often difficult to handle by conventional machine-learning algorithms, which is usually characterized as an aspect of the curse of dimensionality. However, it was shown that some of the arising high-dimensional phenomena can be exploited to increase algorithm accuracy. One such phenomenon is hubness, which refers to the emergence of hubs in high-dimensional spaces, where hubs are influential points included in many k-neighbor sets of other points in the data. This phenomenon was previously used to devise a crisp weighted voting scheme for the k-nearest neighbor classifier. In this paper we go a step further by embracing the soft approach, and propose several fuzzy measures for k-nearest neighbor classification, all based on hubness, which express fuzziness of elements appearing in k-neighborhoods of other points. Experimental evaluation on real data from the UCI repository and the image domain suggests that the fuzzy approach provides a useful measure of confidence in the predicted labels, resulting in improvement over the crisp weighted method, as well the standard kNN classifier. © 2011 Springer-Verlag. | en |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
dc.title | Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification | en |
dc.type | Conference Paper | en |
dc.identifier.doi | 10.1007/978-3-642-23199-5_2 | en |
dc.identifier.scopus | 2-s2.0-80052314494 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/80052314494 | en |
dc.relation.lastpage | 30 | en |
dc.relation.firstpage | 16 | en |
dc.relation.volume | 6871 LNAI | en |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Prirodno-matematički fakultet, Departman za matematiku i informatiku | - |
crisitem.author.orcid | 0000-0003-1946-0384 | - |
crisitem.author.parentorg | Prirodno-matematički fakultet | - |
Appears in Collections: | PMF Publikacije/Publications |
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