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https://open.uns.ac.rs/handle/123456789/13756
Nаziv: | Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification | Аutоri: | Tomašev N. Radovanović M. Mladenić D. Ivanović, Mirjana |
Dаtum izdаvаnjа: | 7-сеп-2011 | Čаsоpis: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Sažetak: | 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. | URI: | https://open.uns.ac.rs/handle/123456789/13756 | ISBN: | 9783642231988 | ISSN: | 03029743 | DOI: | 10.1007/978-3-642-23199-5_2 |
Nаlаzi sе u kоlеkciјаmа: | PMF Publikacije/Publications |
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